final mphil thesis lip cancer latest

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Risk and Treatment Factors for Squamous Cell Carcinoma

of the Lip

A cohort study from the Radiation Oncology Department, Westmead Hospital

Name of Student: Mithilesh Dronavalli

Supervisor: Prof. Val Gebski

Associate Supervisor: A/Prof. Michael J. Veness

Departments:

• National Health and Medical Research Council Clinical Trial Centre, School of

Public Health, Faculty of Medicine, University of Sydney

• Radiation Oncology Department, Westmead Hospital

A thesis submitted in fulfilment of the requirements for the degree of Master of Medical

Philosophy in the School of Public Health, Faculty of Medicine at The University of Sydney.

August 2011

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Acknowledgements

I thank my supervisors for having the patience and endurance to support me

throughout the candidature. I thank my parents and mentors for their moral

support throughout the candidature.

Declaration

I declare that the research presented here is my own original work and has

not been submitted to any other institution for the award of a degree.

Signed: ……………………………………………………………………………

Date: ……………………………………………………………………………….

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Abstract

Patients with lip cancer who have delayed treatment or for whom the cancer is more

aggressive, often have worse outcomes. The aim of this investigation was to find the risk

and treatment factors for developing lip cancer, the recurrence of lip cancer and survival.

This was investigated by a review of the literature and original analysis of data.

A summary of the outcomes regarding survival and recurrence of patients undergoing

surgery or radiotherapy (or combination) was conducted to compare these treatments.

An original analysis of a lip cancer cohort dataset from the Department of Radiation

Oncology at Westmead Hospital was carried out. This included univariate analysis and

multivariate survival analysis investigating time to recurrence and survival. Also

prognostic risk models were developed to classify patients into risk groups in terms of

recurrence and survival.

This investigation adds to the literature as analysis was conducted from a time to

recurrence and survival perspective using survival analysis, rather than just by

investigating the occurrence of the event. Here information regarding the order in which

events occurred is used to make inferences. Also this study gives insight on outcomes of

patients with lip cancer who underwent surgery with adjuvant radiotherapy, where there

is limited information in the literature. It should be noted that there are biases involved in

dealing with a cohort study, especially since patients were not randomised to a

treatment.

In conclusion I have reported on some significant findings regarding treatment

comparisons and risk factors for lip cancer.

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Table of Contents

Risk and Treatment Factors for Squamous Cell Carcinoma of the Lip .............................. 1

Tables .................................................................................................................................... 7

Figures ................................................................................................................................. 10

Abbreviations and acronyms ................................................................................................ 12

Literature review .......................................................................................................... 15

Introduction ......................................................................................................................... 15

TNM, staging and grading .................................................................................................... 16

Grading ................................................................................................................................ 18

Epidemiology ....................................................................................................................... 18

Risk factors .......................................................................................................................... 20

Sun exposure .......................................................................................................................... 20

Smoking as a risk factor for developing disease ..................................................................... 23

Other risk factors for developing lip cancer ........................................................................... 23

Progression of disease ......................................................................................................... 24

Treatment modalities and regimens ..................................................................................... 24

Surgery .................................................................................................................................... 25

Radiotherapy .......................................................................................................................... 27 Summary of treatment outcome .......................................................................................... 29

Flowchart of articles ............................................................................................................... 30

Recurrence ........................................................................................................................... 35

Age .......................................................................................................................................... 36

Gender .................................................................................................................................... 37

Tumour size ............................................................................................................................ 38

Histological grade ................................................................................................................... 42

Maximal tumour thickness ..................................................................................................... 44

Site of lip cancer ..................................................................................................................... 47

Cellular and molecular factors ................................................................................................ 48

Perineural invasion ................................................................................................................. 52

Other risk factors .................................................................................................................... 54

Survival and its risk factors ................................................................................................... 55

Analysis of the Westmead lip cancer dataset ................................................................ 58

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Materials and methods ........................................................................................................ 58

Patient eligibility .................................................................................................................. 58

Inclusion criteria ..................................................................................................................... 59

Exclusion criteria ..................................................................................................................... 59

Treatment ............................................................................................................................ 59

Methods .............................................................................................................................. 59

Methods of univariate analysis ............................................................................................ 60

Methods for adjusted treatment effect ................................................................................ 62

Methods of risk models ........................................................................................................ 62

Dataset description .............................................................................................................. 64

Results ......................................................................................................................... 67

Baseline demographics ........................................................................................................ 67

Dichotomous variables used in overall survival modelling ..................................................... 68

Univariate models ................................................................................................................ 69

Survival models from diagnosis .............................................................................................. 69

Interpretation of risk reduction .............................................................................................. 70

Recurrence models from diagnosis ........................................................................................ 74

Multivariate analysis ............................................................................................................ 76

Treatment comparison: Patients treated with Sx vs. RTx ....................................................... 76

Treatment comparison: Patients treated with Sx or Sx+RTx compared to RTx ...................... 79 Treatment comparison: Patients receiving Sx+RTx vs. Sx. ...................................................... 84

Treatment comparison: Patients receiving Sx+RTx vs. RTx .................................................... 87

Risk modelling ..................................................................................................................... 91

Survival model with treatment ............................................................................................... 93

Survival model not including treatment ................................................................................. 97

Recurrence model with treatment ....................................................................................... 101

Discussion .................................................................................................................. 105

Tumour size ....................................................................................................................... 106

Age at diagnosis ................................................................................................................. 108 Treatment comparison: Sx vs. RTx ...................................................................................... 109

Treatment comparison: Sx and Sx+RTx vs. RTx ................................................................... 111

Treatment comparison: Sx+RTx vs. Sx ................................................................................ 112

Treatment comparison: Sx+RTx vs. RTx .............................................................................. 113

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Risk models ........................................................................................................................ 114

Risk model: Survival with treatment ..................................................................................... 115

Risk model: Survival without treatment ............................................................................... 116

Risk model: Recurrence with treatment ............................................................................... 117

Conclusion .................................................................................................................. 118

Bibliography ............................................................................................................... 120

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Tables

Table 1 Treatment outcome and treatment modality for each article ................................... 31

Table 2 Summary of results relating to loco-‐regional control ................................................ 33

Table 3 Summary of results relating to overall survival .......................................................... 33

Table 4 Summary of results relating to cause-‐specific survival .............................................. 34

Table 5 Summary of results relating to disease free survival ................................................. 34

Table 6 Summary of findings for age. ..................................................................................... 36

Table 7 Summary of results for tumour size ........................................................................... 38

Table 8 Summary of results for histological grade .................................................................. 42

Table 9 Summary of results for maximal tumour thickness ................................................... 44

Table 10 Summary of results for site of lip cancer ................................................................. 47

Table 11 Summary of results for cellular and molecular factors ............................................ 48

Table 12 Summary of results for perineural invasion ............................................................. 52

Table 13 Summary of results for ulcerated pattern and tumour area .................................... 54

Table 14 Risk factors predicting survival in lip cancer in one study ........................................ 57

Table 15 Treatment definitions .............................................................................................. 65

Table 16 Patient and tumour predictor definitions ................................................................ 66

Table 17 Summary measures on age of patients by treatment groups .................................. 67

Table 18 Baseline dichotomised variables and all cause mortality ......................................... 68

Table 19 Univariate results for overall survival ...................................................................... 69

Table 20 Univariate results for recurrence modelling ............................................................ 74

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Table 21 Survival and recurrence models for the treatment comparison between patients

treated with Sx alone vs. RTx alone ........................................................................................ 76


treated with Sx alone or with adjuvant RTx vs. RTx alone ...................................................... 79

Table 23(a) Time dependent Cox analysis at 24 months ........................................................ 82

Table 23(b) Summary of patients; based on 2 yr survival. ...................................................... 83

Table 24 Adjusted survival and recurrence models for the treatment comparison between

patients receiving Sx+RTx vs. Sx. ............................................................................................. 84


patients receiving Sx+RTx vs. RTx ........................................................................................... 87

Table 26 Time dependent Cox analysis at 24 months ............................................................ 90

Table 27 Proportional hazards model for the survival risk model including treatment

comparison ............................................................................................................................. 93

Table 28 2x2 table for risk grouping ....................................................................................... 94

Table 29 Logrank test validating the risk group cut-‐off point ................................................. 95

Table 30 Gronnesby-‐Borgan goodness of fit test ................................................................... 95

Table 31 May-‐Hosmer goodness of fit test ............................................................................. 97

Table 32 Proportional hazards model for the survival risk model excluding treatment

comparison ............................................................................................................................. 98

Table 33 Chi-‐squared test for risk grouping ............................................................................ 98

Table 34 Logrank test validating the risk group cut-‐off point ................................................. 99

Table 35 Gronnesby-‐Borgan goodness of fit test ................................................................. 100

Table 36 May-‐Hosmer goodness of fit test ........................................................................... 100

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Table 37 Proportional hazards model for the recurrence risk model including treatment

comparison ........................................................................................................................... 101

Table 38 Chi-‐squared test for risk grouping .......................................................................... 102

Table 39 Logrank test for the risk group cut-‐off point. ......................................................... 102

Table 40 Gronnesby-‐Borgan goodness of fit test ................................................................. 104

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Figures

Figure 1 Incidence rates in Asia, Europe and USA for lip cancer. ....................................... 19

Figure 2 Meyer’s plasty: steps involved to excise a lesion .................................................. 26

Figure 3 Flowchart of articles assessing treatment outcomes for lip cancer ...................... 30

Figure 4 Cumulative proportion experiencing the event for the tumour size as a predictor

of survival ............................................................................................................................ 72

Figure 5 Cumulative proportion experiencing the event for the variable of age (age≥70

years) as a prognostic indicator of survival ......................................................................... 73

Figure 6 Cumulative proportion experiencing the event for Sx alone vs. RTx alone in

predicting overall survival ................................................................................................... 77

Figure 7 Cumulative proportion experiencing the event for Sx alone vs. RTx alone in

predicting time to recurrence. ............................................................................................ 78

Figure 8 Cumulative proportion experiencing the event for Sx or Sx+RTx vs. RTx alone in

predicting survival ............................................................................................................... 81


predicting recurrence. ........................................................................................................ 83

Figure 10 Cumulative proportion experiencing the event for Sx+RTx vs. Sx alone in


Figure 11 Cumulative proportion experiencing recurrence for Sx+RTx vs. Sx .................... 86

Figure 12 Cumulative proportion experiencing the event for Sx+RTx vs. RTx alone in



predicting recurrence ......................................................................................................... 90

Figure 14 Risk model of survival for patients who have been treated ............................... 96

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Figure 15 Risk model of survival for patients diagnosed and awaiting treatment ............. 99

Figure 16 Risk model of time to recurrence ..................................................................... 103

Note: A P value less than P = 0.05 is considered significant in this thesis.

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Abbreviations and acronyms

Terms -‐ Definitions

2x2 table – Two-‐by-‐two table

95%CI -‐ 95% Confidence Interval

ANZ -‐ Australia and New Zealand

BT -‐ Brachytherapy

Cat. -‐ Categorical

cm -‐ Centimetres

cont. -‐ Continuous

corr. -‐ Correlation

CSS -‐ Cause specific survival

DFS -‐ Disease free survival

Diff -‐ Differentiated

DRR -‐ Delayed regional recurrence

EBRT -‐ External beam radiotherapy

FUP -‐ Followup

GB-‐ Gronnesby-‐Borgan

HDR -‐ High dose rate

HR -‐ Hazard ratio

KM -‐ Kaplan Meier

LDR -‐ Low dose rate

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LR -‐ Local recurrence

LRC -‐ Locoregional control

Mets -‐ Metastases

MH -‐ May and Hosmer goodness of fit test

mm -‐ millimetres

MTT -‐ Maximal tumour thickness

No. -‐ Number

NSW -‐ New South Wales

OR -‐ Odds ratio

OS -‐ Overall survival

PCNA -‐ Proliferating cell nuclear antigen

RCT -‐ Randomised control trial

RTx -‐ Radiotherapy

SA -‐ South Australia

SCC -‐ Squamous cell carcinoma

SEER -‐ Surveillance, Epidemiology and End Results

Sx -‐ Surgery

Sx+RTx -‐ Surgery and adjuvant radiotherapy

TNM -‐ Tumour, node and metastasis

UICC -‐ International union against cancer

USA – United States of America

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UV – Ultraviolet

UVB -‐ Ultraviolet B

XP -‐ Xeroderma pigmentosum

yrs -‐ Years

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Risk and Treatment Factors for Squamous Cell Carcinoma of the Lip

A cohort study from the Radiation Oncology Department, Westmead Hospital

Literature review

Introduction

Lip cancer is a malignant neoplasm of the upper or lower lip, or commissure and

vermillion border, or inner aspect of the lip (1) and is classified according to the

International Classification of Disease as 140.0-‐140.9 ICD-‐9. In some studies lip cancer

accounts for up to 25% of oral cancers (2) although, at least in Australia, lip cancer is

better classified as a sun exposure induced cancer rather than a smoking related oral

cancer. Lip cancers account for <5% of head and neck cancers after excluding other non-‐

melanoma skin cancer.(2) Histologically 90% of lip cancers are of squamous cell origin,

with the remaining 10% comprising of basal cell carcinoma and adenocarcinoma. In this

thesis I will focus on squamous cell carcinoma (SCC) of the lip and this is implied by use of

the term lip cancer unless expressed otherwise.

Lip cancer may follow an indolent time course and have a favourable outcome if treated

in a timely and appropriate fashion, however in a subset of patients the cancer can be

aggressive, with increased morbidity and mortality often associated with the subsequent

development of nodal metastases.(3) If these patients are identified and treated early,

the likelihood of cure is increased. It is therefore important to identify the risk factors for

lip cancer and to investigate the effect of treatment options in order to improve outcome.

The objectives of this thesis are to discuss the risk factors for lip cancer in terms of the

risk of developing disease, recurrence and survival. Risk factors will be presented as either

patient or tumour factors. Treatment factors for prognosis will also be investigated.

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The risk factors for developing lip cancer, recurrence and predicting survival are discussed

in the first chapter. Treatment comparisons between radiotherapy (RTx), surgery (Sx) and

surgery and adjuvant radiotherapy (Sx+RTx) are also investigated and presented in

Chapter 1. In Chapter 2, risk factors for recurrence and survival are examined via a series

of survival analyses, both univariate and multivariate. Treatment comparisons are

assessed univariately and adjusted for confounding variables, and risk models were

developed in order to assess the risk of recurrence and survival. Risk models were

constructed to classify patients into risk groups based on baseline risk (patient and

tumour factors only) and post treatment risk (patient, tumour and treatment factors).

This study aims to provide a model that could aid the understanding of the factors

involved in lip cancer, and the effect of different treatment options on recurrence and

survival. However, this study has inherent selection and referral bias, which will be

discussed later, and can therefore not be expected to provide a high level of evidence.

Note that to my knowledge there have been no published randomised control trials

(RCTs) on lip cancer.

TNM, staging and grading

The following is a summary of the Tumour, Node and Metastasis (TNM) classification for

lip cancer from the International Union against Cancer (UICC).(4)

I. Codes describing the tumour

TX: primary tumour cannot be assessed

T0: no evidence of primary tumour

Tis: carcinoma in situ

T1: tumour less than 2 centimetres (cm) in greatest dimension

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T2: tumour more than 2 cm but not more than 4 cm in greatest dimension

T3: tumour more than 4 cm in greatest dimension

T4: tumour invades adjacent structures (mandible, tongue musculature, maxillary sinus,

skin)

II. Codes describing nodal involvement

NX: regional lymph nodes cannot be assessed

N0: no regional lymph node metastasis

N1: metastasis in a single ipsilateral lymph node, less than 3 cm in greatest dimension

N2a: metastasis in a single ipsilateral lymph node, more than 3 cm but not more than 6

cm in greatest dimension

N2b: metastasis in multiple ipsilateral lymph nodes, none more than 6 cm in greatest

dimension

N2c: metastasis in bilateral or contralateral lymph nodes, none more than 6 cm in

greatest dimension

N3: metastasis in a lymph node, more than 6 cm in greatest dimension

III. Codes describing metastasis

M0: no distant metastasis

M1: distant metastasis

IV. Stage Grouping

Stage I: T1N0M0

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Stage II: T2N0M0

Stage III: T3N0M0; T1 or T2 or T3N1M0

Stage IV: T4N0 or N1M0; Any T, N2, or N3M0; Any T, any N, M1

Grading

The Broder’s grading system (5) is the main system used in lip cancer studies to assess

histological grading of tumour specimens. This system categorises tumours according to

well, moderate and poor differentiation. The potential weakness with this system is that

the degree of differentiation may vary across any surgical specimen.(6) However, some

studies have shown correlation between tumour grading and prognosis.

In contrast, the Anneroth and Jacobson system includes the degree of keratinisation,

polymorphism, mitoses, inflammatory infiltration and mode of invasion. These 5 factors

are graded out of 4 and total scores are divided into grade I (0-‐4), grade II (5-‐10), grade III

(11-‐15) and grade IV (16-‐20).(7)

These two systems are mentioned here, as when discussing later articles, histological

grading will be assessed via these two systems.

Epidemiology

The epidemiology of lip cancer is investigated here from both an Australian and

international perspective. In Australia one large study in the literature reporting the

epidemiology of lip cancer was undertaken in South Australia (SA).

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The age-‐standardised incidence of lip cancer in SA between 1976 -‐ 1996 was 15/100,000

in males and 4/100,000 in females.(8) The authors considered this very high on a global

scale. Over the follow up period there were 2095 (77.1%) males and 621 (22.9%) females

presenting with lip cancer (8) and as of June 2005 there were 1.54 million residents in

SA.(9) The average age for diagnosis was 58.3 yrs in males and 66.0 yrs in females.(10)

The sun exposed lower lip was the most common site (72.5% lower lip vs. 7.7% upper lip

vs. 19.8% remaining).(8) New South Wales (NSW) has a much lower incidence in line with

global rates at 3.8/100,000 for males and 1.5/100,000 for females during 2005.(10)

Figure 1 Incidence rates in Asia, Europe and USA for lip cancer.

Figure courtesy of Yako-‐Suketomo et al, 2008 (11)

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In Figure 1 the contrasting trends in the incidence of lip cancer in Asia, Europe and the

United States of America (USA) can be seen. The incidence has been falling in those

countries with the incidence higher than 2/100,000 at 1973 in parts of England, Italy, and

Denmark and in white Americans. Since the 1970s there has been a marked decrease in

the incidence of lip cancer in many countries as a consequence of a better awareness of

smoking and UV exposure as causes for lip cancer. The East Asian locations studied all

have a low incidence of lip cancer. Also black Americans have a much lower incidence

than white Americans, likely due to the increased melanin found in dark skin that is UV

protective.

Risk factors

The risk factors for developing lip cancer can be defined as environmental, behavioural or

endogenous. Environmental risk factors consist of ultraviolet (UV) sunlight exposure and

rural residence. Behavioural risk factors include smoking (including pipe smoking in

particular), occupation, alcohol consumption, socioeconomic status and viral infections

(e.g. human papilloma virus). Endogenous factors include familial and genetic

predisposition, immunosuppression and immunodeficiency. Race and cultural practices

are other risk factors.

Sun exposure

Sunlight exposure is a major risk factor in developing lip cancer in Australia, and is a result

of a cumulative lifetime exposure to sunlight. UVB (wavelength of 290-‐320nm) is the key

exposure attributed to lip cancer. UVB radiation induces mutational changes in the DNA

that can lead to cancerous growth. In particular the p53 tumour suppressor gene that

would otherwise terminate cancerous growth is mutated and rendered ineffective.(12)

Risk of lip cancer associated to sunlight exposure is influenced by outdoor exposure, fair

skin (fair skin has a lack of melanin which protects against UVB), increasing age (lifetime

sun exposure), gender (males associated with higher outdoor exposure), use of sun

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protection and rural-‐urban divide and cultural practices.(12) In SA over the period of

1996-‐1997 the incidence in rural areas was 12.3/100 000, compared to 5.9/100 000 in

metropolitan Adelaide.(13) This is likely to be due to increased outdoor sun exposure for

the rural population living in SA.

Lip cancer has a higher incidence in men than in women, which was seen in both the SA

and NSW studies.(8, 10) Other countries, like the USA (14) and Greece (15) confirm a

similar trend, which has been attributed to higher exposure of men than women to UVB,

as well as other carcinogens, such as cigarette smoke.

For example, in Greece, the male: female ratio was 9.2:1 for lip cancer, which was

attributed to women mostly staying in an indoor environment compared to men. Females

when working outdoors used a covering for their face and men generally did not. Also

they noted that the diagnosis of lip cancer occurred on average, 11.2 yrs later in females

than males. At the time of the study, the incidence of smoking in females was much lower

than in males. Furthermore, in the 897 patients of the study 80% were from a rural area.

Rural residents doing agricultural work would have had more sun exposure then their

urban counterparts. Recently the overall incidence of lip cancer has reduced in Greece

with increased public awareness, decreased pipe smoking, decreased outdoor workers

and the rural-‐urban drift.(15)

In a USA study of lip cancer African-‐Americans comprised only 7% of the study, which

suggests a low incidence of lip cancer in this race.(16) Furthermore in Figure 1 from the

Surveillance, Epidemiology and End Results (SEER) study the incidence was higher among

white Americans compared to African-‐Americans. African-‐Americans have significantly

more melanin in their skin than the white population so they are likely more protected

against UV light and developing skin cancer.(17) Among African-‐Americans and white

Americans living in the same area and assumedly receiving similar UV exposure, African-‐

22

Americans have a lower incidence of lip cancer. Therefore, this likely implies that the

protection by melanin from the damaging effects of UVB results in a lower incidence of lip

cancer among African-‐Americans.

There has been a case study of a 15-‐year-‐old patient with xeroderma pigmentosum (XP)

diagnosed with lip cancer.(18) XP is a rare genetic disorder where there is a deficiency in

the ability to repair DNA mutations induced by UV light. This further adds to the evidence

that sun-‐exposure is a risk factor for the development lip cancer. This is because if lip

cancer is triggered by mutations induced by UV exposure then those with XP due to their

deficiency in repairing such mutations can develop both skin cancer and also lip cancer at

a much younger age.

Lip cancer affects mainly older patients, with only 97 of a cohort of 1038 (7%) patients

aged under 40 years old.(19) Of these 97 patients, 63 reported prolonged sun exposure

based in their work environment. Cumulative sun exposure increases with age and

therefore patients under the age of 40 generally have a lower incidence of lip cancer.

However these particular young patients may have developed lip cancer in part due to

excessive sun exposure that they experienced. The mean age for developing lip cancer

was above 58 for both sexes in one study supporting this disease occurring in older

patients.(10) Another study also reported only 14 patients out of 223 below the age of 50

(6.3%).(20) Lip cancer can therefore be considered a cancer of patients in their 60 -‐ 70’s.

Fabbrocini et al., 2000 (21) noted that p53 expression was elevated in lip cancer

specimens compared to samples of the lip from non-‐cancer controls (Lip cancer: 50%,

control: 20%). This is an important observation because p53 expression increases in

chronically UV exposed areas that develop lip cancer. As this is an observational study (a

snapshot), we cannot say whether the controls will go on to develop lip cancer with time.

This finding is unlikely to aid clinicians in treating lip cancer as diagnosis is made on

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clinical presentation and histological findings and not by p53 expression. Currently

biopsies are not done on at risk individuals as a screening tool to assess p53 expression.

This study particularly did not add support to using p53 as a screening tool as it is not a

cohort study that investigates cause and effect over time.

Smoking as a risk factor for developing disease

Lip cancer is strongly associated with smoking in some studies in some countries, in

particular pipe smoking.(2) This may be due to the local toxicity of smoking. Smoking has

also been linked with lung cancer (22) and the rates of lung cancer are reported to be

higher in lip cancer patients than in the general population.(12) Therefore, smoking has

causality with both lip cancer and lung cancer.

In the previously mentioned study of patients below 40 years of age, 78 out of 97 patients

used tobacco (80.4%) a prevalence much higher than the general population.(19) This

implies that those aged below 40 years, who had less lifetime sun exposure, developed lip

cancer possibly due to the damaging effect of smoking.

Other risk factors for developing lip cancer

The less common risk factors of immunosuppression or immunodeficiency are important

to consider and are particularly relevant to the younger population. Many cases are

reported in young patients who have had renal transplants and due to the anti-‐rejection

medication are immunosuppressed.(23) In these patients the cancer is often more

biologically aggressive due to host susceptibility.

A study of renal transplant recipients identified age, time since transplant, current use of

azathioprine, cyclosporine, male sex and birthplace outside Australia and New Zealand

24

(ANZ) to be significantly associated with an increased incidence of lip cancer.(23) The data

was obtained from the ANZ Transplant Registry between 1982 and 2003 with a sample

size of 8162 renal transplant patients. The variables of interest are the

immunosuppressant agents and time since transplant as they reflect the degree of

immunosuppression in the patient.

In other studies increased alcohol consumption was also associated with lip cancer (21) as

was low education level.(24) The hypothesis being that a low education level could be

associated with heavy outdoor work and increased sun-‐exposure and also an increased

prevalence of smoking.

Progression of disease

The clinical precursors to lip cancer predominantly are leukoplakia, hyperkeratosis, and

actinic changes and are related to sun exposure.(25, 26) The initial presentation is

variable but may be that of an ulcer, usually of the lower lip, that fails to heal and

gradually increases in size and thickness. Pain is often not an issue with the patient. Only

a small proportion (5-‐10%) will actually present with concomitant upper neck

lymphadenopathy from metastatic spread. Instead subsequent nodal relapse is the most

common scenario for nodal metastasis.

Treatment modalities and regimens

There are various treatment options available to a patient diagnosed with lip cancer in its

different presentations. Standard treatment recommendation is either RTx or Sx. Post

operative (or adjuvant) RTx after Sx is also prescribed, especially where the margins of

excision are close or positive.(27) There are various operations utilised and these depend

on the size of the tumour and its localisation, as well as patient, surgeon and institute

preferences. There are also various RTx modalities, which include orthovoltage,

megavoltage (external beam radiotherapy [EBRT]) and brachytherapy (BT). BT may be

25

delivered as either low dose rate (LDR) or high dose rate (HDR) BT, which specifies the

rate of radiation administered. All these treatment options will be described with a

discussion of various treatment regimens currently utilised.

Surgery

Superficial lip cancer with maximal tumour thickness (MTT) less than 3 millimetres (mm)

and also the pre-‐malignant condition of actinic cheilitis may be indications for

vermilionectomy. Actinic cheilitis has a probability of developing into lip cancer if left

untreated.(25) Vermilionectomy is the excision of the vermilion surface of the lip and is

commonly referred to as a lip shave.

For lesions measuring approximately 2 cm or less in maximum dimension, the most

efficacious resection is a “V” shaped wedge excision and primary closure. Here the

excision is in a V shape around the lesion and closure is performed on the two edges. If

the V excision approaches the mental crease, then a “W” excision is performed using the

same principles. Margins of 5 to 7 mm are recommended, with a total resection

achievable of approximately one-‐third of the lower lip.

There are other more sophisticated and complex lip cancer operations including the Abbe

method and the Estlander method. These operations are undertaken when the excision

defect is 30 to 65% of the lip. For defects larger than 65% there is the Bernard-‐Freeman-‐

Fries method. These methods leave very little of the lower lip remaining (1.5 cm) and

therefore, reconstruction using various flaps are utilised, such as the radial forearm-‐

palmaris longus tendon flap.(28) This flap can be used when the expected defect is

greater than 80% of the lower lip. An improved flap for this situation is the anterolateral

thigh flap, which has an inconspicuous scar compared to the forearm and it is then

unnecessary to sacrifice one of the two arteries of the hand.(28)

26

One consequence of many, but not all operations, apart from poor cosmesis, is

microstomia where the oral opening is reduced. This is especially a problem when a

patient has dentures fitted.(25)

A further operation is Meyer’s plasty. This operation can be used for defects up to 80%

and does not require a flap (see Figure 2). In this method, cosmesis was reported as

acceptable in 87% patients with 100% local control in one small study.(29)

Figure 2 Meyer’s plasty: steps involved to excise a lesion

a Tumour. b Tumour excision. c Commissuroplasty: triangular cutaneous excision.

d Mucosal flap incision and lower lip closure, blue arrows. e Eversed mucosal flap, yellow

arrows. f End result with scars along the white line and labiomental crease

Figure courtesy of Jaquet et al, 2005 (29)

27

Other operations include using double free flaps (30) for increased mobility. Some

clinicians recommend Sx as the best option since the margin status of the excision can be

assessed and a detailed histological examination can be performed.(31) However the

functional and cosmetic outcome of any operation must always be taken into

consideration. Patients are often elderly and when given the option some patients may

also elect a non-‐surgical treatment. Patients may also have medical co-‐morbidity that

precludes Sx.

Radiotherapy

RTx offers a non-‐surgical option for treating patients with lip cancer. The mainstay of RTx

to treat lip cancer is orthovoltage energy photons. RTx is a weekday out patient

treatment taking 10-‐15 minutes to deliver. Typical treatments extend over 2-‐6 weeks (10-‐

30 treatments). Shorter treatments are often considered in older sicker patients. Various

dose schedules are also used with one study reporting 17 daily fractions of 300 centi-‐Gray

(cGy) over 4 weeks of orthovoltage as biologically equivalent to 6000cGy in 30 daily

fractions of 200 cGy each, 5 times per week, for 6 weeks of megavoltage therapy. This is

also equivalent to an implant used in BT of 6000cGy with a LDR of 40-‐80 cGy/hr. This

equivalence is in terms of radiobiological equivalence of dose.(32)

BT is less commonly used in Australia in treating patients non-‐surgically. However when

used, one approach uses radioactive iridium-‐192 wires with 3 wires inserted in a

triangular fashion with the dose rate pre-‐calculated before treatment. The mean

calculated dose in one study was 63.54 cGy/hour.(32) The total dose varied between

6000-‐7000 cGy for this study, with treatment completed in 3 to 7 days. The wire pierces

the tumour and the surrounding lip to deliver radiation directly to the tumour. The

procedure is usually carried out under local anaesthetic with the patients spending 3-‐5

days in a radio-‐protective room for LDR BT.

28

An accepted advantage of RTx is that it does not require the tumour to be excised and

hence may result in better cosmesis and functional outcomes compared to Sx. This is

especially true in larger lesions where a significant amount of the lip may need to be

resected. The choice between EBRT and BT is based on physician and patient preferences

and what is available at the treating institution, however, few centres in Australia use BT

for treating lip cancer.

Deeply infiltrating tumours where surgical margins are ill-‐defined, may make simple

excision difficult and it is these cases where Sx is less ideal. A more extensive surgical

approach may lead to less than ideal cosmetic and functional results. In such patients

there is a reasonable likelihood that adjuvant RTx will be recommended, as surgical

margins are often close or positive.

For patients with large tumours and for whom Sx is not advisable, or those who would

have poor functional outcome, RTx is often recommended. This often means RTx treated

patients in many observational studies have more advanced disease possibly leading to a

selection bias when reporting results.(33)

Patients treated with RTx usually tolerate their treatment well, even older patients. When

treating the lip, EBRT irradiates a relatively small volume of surrounding normal tissue,

which usually leads to symptomatic local mucocutaneous reactions. However these

reactions are localised and usually resolve in 4-‐6 weeks following completion of

treatment. Systemic side effects are negligible. Late side effects are limited to the

irradiated lip and many include hypo/hyperpigmentation of the lip and skin with

associated epithelial atrophy. Serious late effects are rare.

29

Summary of treatment outcome

Various studies have reported treatment outcomes following Sx or RTx for patients with

lip cancer. The outcome measures from these studies include loco-‐regional control (LRC),

overall survival (OS), cause specific survival (CSS) and disease free survival (DFS).

LRC is defined as the percentage of lip cancer patients that did not relapse either locally in

the lip or regionally to the nodes. DFS refers to the percentage of the cohort that did not

relapse locally, regionally, distantly or develop a second primary. DFS and LRC differ in

that a metastasis to a distant site is counted in DFS where it is not counted in LRC.

OS is the percentage of the cohort surviving, i.e. not dying of any cause. CSS or

determinate survival is calculated using various methods but refers to the percentage of

the cohort who have not died due to the disease.

This section of the thesis aims to summarise treatment results and make comparisons

between different treatments. There are various weaknesses in many retrospective

studies noting that as most studies do not have two treatment groups for direct

comparison but often just describe the outcome of either Sx or RTx as a single modality

treatment.

Following a literature review articles were selected from the main medical databases

(PubMed, Science Direct and Embase, etc.). The search criteria was as follows: Lip AND

(Carcinomas or Cancer or SCC or Neoplasm) AND (survival or patients or cases). All

abstracts were deidentified and had the results removed by an external researcher. I

excluded all non-‐related articles and sent this list to my supervisor who checked if any of

them should be re-‐included. From this selection process 76 articles remained. The

flowchart of included articles is presented in Figure 3.

30

76 articles Identified

56 articles remaining



24 articles on

Sx

15 articles on

RTx

6 articles on Sx+RTx

7 articles on BT

20 articles excluded (see below *)

7 articles had no data in required format (i.e. LRC, OS, CSS, DFS)

8 articles had only non-‐treatment specific data 41 articles

remaining

6 articles had no 5-‐year data available

Outcome

LRC – 10

OS -‐ 15

CSS – 10

DFS -‐ 6

Outcome

LRC – 6

OS -‐ 10

CSS – 3

DFS -‐ 4

Outcome

LRC – 4

OS -‐ 2

CSS – 0

DFS -‐ 0

Outcome

LRC – 7

OS -‐ 5

CSS – 2

DFS -‐ 4

Flowchart of articles

Figure 3 Flowchart of articles assessing treatment outcomes for lip cancer

*4 had no lip specific data (only oral), 2 epidemiological studies without usable data, 1 basal cell carcinoma, 6 advanced

disease but not at primary presentation, 1 review article without original data, 1 site other than lip, 2 duplicate or

obsolete studies, 1 chemotherapy only, 2 abstracts with no data (of which 1 article was in foreign language with an

English abstract) (total 20). Sx: Surgery; RTx: Radiotherapy; Sx+RTx: Surgery and adjuvant radiotherapy; BT:

Brachytherapy; LRC: Locoregional Control; OS: Overall Survival; CSS: Cause Specific Survival; DFS: Disease Free Survival

Of the 35 articles remaining in Figure 3 many reported more than one outcome and some

articles reported on more than one treatment also. In Table 1 the outcomes reported and

treatments used are listed with the years of study.

31

Table 1 Treatment outcome and treatment modality for each article

Title Reference Years of Study

n OS DFS CSS LRC Brachy-‐ therapy

Sx RTx Sx+RTx

A comparison of results after radiotherapy and surgery for stage I squamous cell carcinoma of the lower lip

de Visscher et al, 1999 (34) 1980-‐1994 256 Yes Yes No No No Yes Yes No

A study of squamous cell carcinoma of the lip at West Virginia University Hospitals from 1980-‐2000

Wilson et al, 2005 (35) 1980-‐2000 52 No No No Yes No Yes No Yes

Brachytherapy for lower lip epidermoid cancer tumoral and treatment factors influencing recurrences and complications

Beauvois et al, 1994 (36) 1972-‐1991 237 Yes No Yes Yes Yes No No No

Brachytherapy for squamous cell carcinoma of the lip

Tombolini et al, 1998 (37) 1970-‐1992 57 Yes Yes No Yes Yes No No No

Cancer of the lips Results of the treatment of 299 patients

Cowen et al, 1990 (38) 1970-‐1985 299 No No No Yes Yes No No No

Carcinoma of the lip Heller et al, 1979 (39)

1955-‐1969 171 Yes No Yes Yes No Yes No No

Carcinoma of the lip Petrovich et al, 1979 (40)

1945-‐1975 250 Yes No No Yes No No Yes No

Choice of the treatment for lip carcinoma–an analysis on 74 cases

Wu et al, 1985 (41)

1958-‐1974 74 Yes No No No No Yes Yes No

Critical review of 121 squamous cell epitheliomas of the lip

Giuliani et al, 1989 (42)

1974-‐1986 121 Yes Yes No Yes No Yes No No

Curative radiotherapy for early cancers of the lip, buccal mucosa, and nose–a simple interstitial brachytherapy

Ngan et al, 2005 (43)

1996-‐2004 13 Yes Yes Yes Yes Yes No No No

Effectiveness of brachytherapy in the treatment of lip cancer a retro at the Istanbul university oncology institute

Aslay et al, 2005 (44)

1988-‐2003 41 Yes Yes No Yes Yes No No No

Interstitial brachytherapy for carcinomas of the lower lip Results of treatment

Orecchia et al, 1991 (45) 1973-‐1988 47 Yes Yes No Yes Yes No No No

Lip cancer experience in Mexico. An 11-‐year retrospective study

Luna-‐Ortiz et al, 2004 (46) 1990-‐2000 113 Yes No No No No Yes Yes No

Long term results in treating squamous cell carcinoma of the lip, oral cavity and orophar

Hemprich et al, 1989 (47) 15 years 352 Yes No No No No No Yes No

Lymph-‐node metastasis in squamous cell carcinoma of the lip

Califano et al, 1994 (48)

1975-‐1987 105 Yes No Yes No No Yes No No

Management of lower lip cancer a retrospective analysis of 118 patients and review of the literature

Bilkay et al, 2003 (18) 1983-‐1999 118 Yes No Yes Yes No Yes No No

n: number of patients in study, OS: Overall survival, DFS: Disease free survival, CSS: Cause

specific Survival, LRC: Loco-‐regional control, Sx+RTx: Surgery and adjuvant radiotherapy

32

Title Reference Years of Study n OS DFS CSS LRC Brachy-‐ therapy Sx RTx Sx+RTx

Meyer’s surgical procedure for the treatment of lip carcinoma

Jaquet et al, 2005 (29) 1983-‐2001 24 Yes No Yes Yes No Yes No No

Oncologic aspects of the vermilionectomy in squamous cell carcinoma of the lower lip abstract

van der Wal et al, 1996 (49)

1985-‐1992 14 No No No Yes No Yes No No

Outcome analysis for lip carcinoma

Zitsch et al, 1995 (2) 1940-‐1987 1252 Yes No Yes No No Yes Yes No

Prognostic factors in squamous cell carcinoma of the oral cavity.

Beltrami et al, 1992 (50)

80

Yes No Yes No No Yes No No

Radiotherapy for cancer of the lip

Gooris et al, 1998 (32)

1974-‐1994 85 No Yes No Yes Yes No Yes Yes

Results of radiation therapy of cancer of the lip

Miltenyi et al, 1980 (51) 170 Yes No Yes No No No Yes No

Results of radiotherapy for scc lower lip. A retrospective analysis of 108 patients

de Visscher et al, 1996 (52)

1980-‐1992 108 Yes Yes No No No No Yes No

Squamous carcinoma of the lower lip in patients under 40 years of age

Boddie et al, 1977 (19) 1943-‐1974 1308 Yes No Yes No No Yes Yes No

Squamous cell carcinoma of the lip: a retrospective review of the Peter MacCallum Cancer Institute experience 1979-‐88

McCombe et al, 2000 (33)

1979-‐1988 323

No No No Yes No Yes Yes No

Squamous cell carcinoma of the lip analysis of the Princess Margaret Hospital experience

Cerezo et al, 1993 (53) 1971-‐1976 117 No No No Yes No Yes Yes Yes

Squamous cell carcinoma of the lip: is there a role for adjuvant radiotherapy in improving local control following incomplete or inadequate excision?

Babington et al, 2003 (27)

1980-‐2000

130

Yes Yes No Yes No Yes Yes Yes

Squamous cell carcinoma of the lip treated with Mohs

Holmkvist et al, 1998 (54)

1986-‐1999 50 No Yes No Yes No Yes No No

Squamous cell carcinoma of the lip

Cruse et al, 1987 (55) 1962-‐1982 117 Yes No Yes No No Yes No No

Squamous cell carcinoma of the lips in a northern Greek population. 5yr Surv rate

Antoniades et al, 1995 (15) 1979-‐1989 906 Yes No No No No Yes Yes Yes

Squamous cell carcinoma of the lower lip and supra-‐omohyoid neck dissection

Kutluhan et al, 2003 (26) 1994-‐2000 31 Yes No Yes Yes No Yes No No

Squamous-‐cell carcinoma of the lower lip a retrospective study of 58 patients

dos Santos et al, 1996 (56) 1980-‐1999 58 Yes Yes No Yes No Yes No No

Surgical treatment of squamous cell carcinoma of the lower lip

de Visscher et al, 1998 (57)

1979-‐1992 184 Yes Yes No Yes No Yes No No

Survival analysis of 5595 head and neck cancers

Rao et al, 1998 (58) 1987-‐1989 62 Yes No No No No Yes Yes Yes

The step technique for the reconstruction of lower lip defects after cancer resection

Blomgren et al, 1988 (59) 25 years 165 Yes No Yes Yes No Yes No No

n: number of patients in study, OS: Overall survival, DFS: Disease free survival, CSS: Cause

specific survival, LRC: Loco-‐regional control, Sx+RTx: Surgery and adjuvant radiotherapy,

33

Table 2 Summary of results relating to loco-‐regional control

Treatment 5yr LRC 95%CI No. of studies No. maintaining LRC Sample size Crude ratio

Sx 89.8% (87.9 – 91.6%) 10 785 947 82.9%

EBRT 85.3% (82.2 – 88.3%) 5 414 504 82.1%

Sx+RTx 95.3% (88.3 – 100%) 4 43 47 91.5%

BT 94.6% (92.8 – 96.4%) 6 579 617 93.8%

95%CI: 95% Confidence interval, No.: Number

Treatment results were pooled together with results weighted according to the inverse

variance. That is larger studies contributed more to the pooled results than smaller

studies, because they have a decreased variance due to the larger sample size. Where the

5yr LRC rate was either 100% or 0% the Wilson interval was used to calculate the

variance.(60) In Table 2 the results for LRC are summarised. The results suggest that

patients undergoing BT may have a slightly better outcome than patients undergoing

either Sx or RTx. Sx may result in better LRC than RTx noting that the 95% CIs do not

overlap. The combination of Sx+RTx may also be better than RTx with CIs touching. Note

that the crude ratio is the number of patients with an outcome (e.g. maintaining LRC)

divided by the total number of patients in the sample (the sample size).

Table 3 Summary of results relating to overall survival

Treatment 5yr OS 95%CI No. of studies No. alive Sample size Crude ratio

Sx 81.9% (80.1 – 83.7%) 15 1146 1550 73.9%

EBRT 79.9% (77.4 – 82.4%) 10 729 943 77.3%

Sx+RTx 72.0% (56.2 – 87.8%) 2 11 18 61.1%

BT 85.3% (81.8 – 88.8%) 4.00 280 357 78.4%


34

Table 3 details the results for OS. Patients having BT had the best outcome, followed by

Sx then RTx and lastly Sx+RTx but note all CIs were overlapping indicating no statistically

significant difference in OS between the 4 treatments.

Table 4 Summary of results relating to cause-‐specific survival

Treatment 5yr CSS 95%CI No. of studies No. not dead of disease Sample size Crude ratio

Sx 94.9% (93.7 – 96.1%) 10 1114 1219 91.4%

EBRT 96.0% (94.3 – 97.8%) 3 401 439 91.3%

Sx+RTx

0 0 0

BT 91.1% (87.5 – 94.8%) 1.00 216 237 91.1%


Table 4 details the results for CSS. Patients receiving RTx achieved the best CSS, followed

by Sx and BT. No studies with Sx+RTx were available. Here also the CIs overlapped for all

treatments suggesting no significant difference in CSS between treatments.

Table 5 Summary of results relating to disease free survival

Treatment 5yr DFS 95%CI No. of studies No. disease free Sample size Crude ratio

Sx 85.0%

(82.4 –

87.6%) 6 486 630 77.1%

EBRT 81.7%

(77.5 –

85.9%) 4 247 314 78.7%

BT 90.2%

(84.9 –

95.4%) 3 105 120 87.5%


35

Table 5 details the results for DFS. Patients undergoing BT had the best outcome,

followed by Sx and then RTx. Once again all CIs overlapped.

It is important to note that these results do not conclusively favour one particular

treatment over the other across the outcomes of LRC, OS, CSS and DFS. Most patients did

not die from their lip cancer so OS may not necessarily be an accurate outcome to

investigate in this disease. Similarly CSS is calculated using deaths due to lip cancer. This

outcome may be biased if people die due to a secondary cause unrelated to lip cancer

(e.g. heart attack), before they may have relapsed and potentially die from their lip

cancer. An alternative way is to analyse this problem is to use competing risk survival

analysis, where the probability of dying due to lip cancer is adjusted for by the presence

of competing co-‐morbid events that precede death due to lip cancer such as other causes

of mortality. However no studies we reviewed have used this statistical methodology.

Recurrence

Following treatment patients may experience recurrence at either the primary site (local

recurrence) or regionally (nodal recurrence). Alternatively, but much less likely, lip cancer

may metastasise to distant sites such as the lung or liver. Delayed regional recurrence

(DRR) implies that regional metastases were not clinically present at the time of diagnosis

but occurred later.

If recurrence does occur, 95% of such cases usually occur within 5 years of treatment.(18)

The peak incidence of recurrence usually occurs in the first and second years. For example

in one study from 1996, 12 out of 108 patients developed local or regional recurrences

and of these 8 occurred within the first 2 years following treatment.(52)

The predictors of recurrence as documented in the literature include: tumour size,

histological grade, MTT, extent of surgical margins (positive/close vs. clear margins),

36

perineural and muscle invasion, age and various cellular and molecular factors. These

predictors will be investigated using the data published by other researchers and

analysing a database of patients from Westmead Hospital, Sydney, Australia.

Predictors of recurrence and survival can be divided into patient, tumour and treatment

factors. Patient factors include age, gender, smoking and UVB exposure from sunlight.

Tumour factors include tumour size, histological grade, MTT, perineural invasion, muscle

invasion, cellular and molecular factors and status of surgical margins. Treatment factors

include treatment comparisons (Sx or RTx).

Age

Table 6 Summary of findings for age

Study Outcome

Cut-‐off

point N Effect size 95%CI P value FUP Event rate

Fernandez et al, 2003 (61)* Mets. Cat. 251 1.013 OR (0.97-‐1.06) 0.05 5 yrs 6.40%

Zitsch et al, 1999 (5) DRR 40yrs 1001 -‐ -‐ 0.99 5 yrs 4%

n: Sample size, 95%CI, 95% Confidence interval FUP: Minimum follow up, Mets: Metastases, DRR: Delayed regional recurrence, Cat. : Categorical, *: multivariate model

There are various hypotheses as to why age may be associated with recurrence. One is

that cancer is likely to recur in older people due to the increased rate of accumulated

somatic genetic mutation with increasing age. Alternatively lip cancer is more biologically

aggressive in the young and hence more likely to recur despite treatment.

Fernandez et al, 2003 (61) as detailed in Table 6 analysed age in a multivariate model

along with site and tumour area and reported a non-‐significant odds ratio (OR) with no P

value given. However the CI for the OR included 1 and this usually implies that the result

37

is statistically non-‐significant. In this study there were 251 patients with a minimum

follow up of 5 yrs and an event rate of 6.4% throughout the study period.

Zitsch et al, 1999 (5) also reported a statistically non-‐significant association between age

and DRR using age as a binary variable with the cut-‐off point at 40 years. This study had

1001 patients and was one of the larger studies dealing with lip cancer. An OR was not

reported. Both these large studies suggest that age alone is not a strong predictor of DRR

or worse outcome.

Gender

The effect of gender on DRR was investigated in 2 studies. These both found no

association between gender and DRR.(5, 62) Of these studies, Zitsch et al, 1999 (5) had

1001 patients with a minimum follow up of 5 yrs and reported the association between

gender and DRR as statistically non-‐significant (P=0.34). The other study of Vukadinovic et

al, 2007 (20) had 223 patients with a median follow up of 56 months and also found no

statistically significant association between gender and DRR. The study did not mention

an OR or P value.

A previously described study found no association between gender and the tumour size

of the primary tumour which is itself an indicator of DRR.(20) Also gender did not impact

on the risk of CSS from lip cancer (i.e. proportion dying of disease).(2)

38

Tumour size

Table 7 Summary of results for tumour size

Study Outcome Cut-‐off point n Effect size 95%CI P value FUP Event rate Zitsch et al, 1999 (5) DRR 3 cm 1001 -‐ -‐ 0.034 5 yrs 4%

Hosal et al, 1992 (3) DRR+LR -‐

-‐ -‐ no corr. -‐ -‐

de Visscher et al, 2002* (57) LR Cat. 184 -‐ -‐ <0.01 2 yrs 6%

de Visscher et al, 2002* (57) DRR Cat. 184 -‐ -‐ >0.05 2 yrs 5%

McGregor et al, 1992 (63) DRR -‐ 108 -‐ -‐ <0.05 2 yrs 18%

Heller et al, 1979 (39) LR -‐ 171 1.01 OR -‐ 0.99 -‐ 8%

Rodolico et al, 2005 (7) DRR T2&T3 vs. T1 97 15.21 HR (2.25-‐94.1) 0.033 5 yrs -‐

Rodolico et al, 2005 (7) DRR cont. 97 1.09 HR (1.05-‐1.13) <0.0001 5 yrs -‐

Rodolico et al, 2005* (7) DRR T2&T3 vs. T1 97 13.5 HR (2.19-‐83) 0.005 5 yrs -‐

Rodolico et al, 2005* (7) DRR cont. 97 1.04 HR (0.99-‐1.09) 0.042 5 yrs -‐

Rodolico et al, 2004 (64) DRR 2 cm 97 -‐ -‐ 0.05 5 yrs -‐

n: Sample size, 95%CI: 95% Confidence interval, FUP: Minimum follow up, DRR: Delayed regional recurrence, LR Local recurrence Cat. : Categorical, *: multivariate model, cont.: continuous, cat: categorical, corr.: correlation

Tumour size is recorded as the maximum lesion size and is the largest diameter of the

tumour. Tumour size is reported in the TNM classification at cut-‐offs of 2 cm (T1), 2-‐4 cm

(T2) and >4 cm (T3). Various studies, as presented in Table 7, have investigated tumour

size as a predictor of regional recurrence. These studies incorporate both the event of

recurrence and time to event, in survival models. Tumour size has an impact on prognosis

and also on selection of the appropriate treatment.

Zitsch et al, 1999 (5) with 39 patients developing DRR found tumour size to be a

statistically significant predictor of DRR (P = 0.034). Tumour size was dichotomised into

above or below 3 cm. Despite the larger size of this study, the power to detect a 50%

difference in prevalence of risk factors between the two tumour size categories was only

very low. This was because the overall event rate was low and also due to the small

number of patients with tumour size above 3cm, who composed only 14% of the total

sample.

39

In contrast Hosal et al, 1992 (3) found that size of the primary did not correlate with

regional recurrence. However, this was inconclusive as the study only reported 1 DRR

with the remaining patients having nodal involvement at presentation. To combine

patients with nodal involvement at diagnosis and DRR is an unreasonable assumption as

they may experience different disease progressions. Those with nodal involvement at

presentation have not undergone treatment whereas; the delayed group has undergone

curative treatment to the primary. Also, clinically the interest lies in predicting DRR so as

to establish an at risk group. By doing so, such patients may warrant different

management such as elective nodal treatment or more intensive follow up and

investigations.

De Visscher et al, 2002 (57) also documented a low event rate of 9 DRR and 11 LR out of

184 treated patients. The study found tumour size to be a statistically significant predictor

of LR on multivariate analysis, but found no association with DRR. DRR is an important

prognosticator for outcome and therefore a determinate of survival.

McGregor et al, 1992 (63) documented a higher event rate (18%, double that of previous

studies) and documented that tumour size (cut-‐off point unspecified, but likely to be

based on T staging) was a statistically significant predictor (P<0.05) of DRR. This study had

19 events out of 108 (18%) with a minimum follow-‐up of 2 years. There were no details of

statistical methods used and no reported effect size.

Heller et al, 1979 (39) looked at tumour size as a predictor of LR but found no statistically

significant association (Table 7). In this study there were 14/171 LR, 8 regional

recurrences and 1 distant metastasis without regional recurrence.(39) The regional

recurrences should have also been included in the analysis; this way the outcome would

have been both local and regional control, which is a more complete outcome. It would

40

also have added more power to an association with tumour size as the event rate

increases.

Rodolico et al, 2005 (7) analysed risk factors predicting the risk of regional recurrence in a

Cox regression model and reported tumour size, analysed as a continuous variable (T size

in mm) and a categorical variable (T staging), to be a significant predictor of DRR on both

univariate analysis and multivariate analysis.(7) The study ran for 11 years (1988-‐1998)

and reported on 97 patients who were followed for 5 years. All patients were treated

with Sx alone. On univariate analysis patients with T stage 2-‐3 had a significantly higher

risk of DRR (HR 15.21, 95% CI 2.25-‐94.1) compared to patients with T1. The risk of DRR

increased by 9% for each mm increase in tumour size (HR: 1.09, 95% CI 1.05-‐1.13). After

adjusting for other risk factors this association was only slightly attenuated with the HR

for tumour stage being 13.50 and that for tumour size being 1.04. The multivariate

models were adjusted for molecular factors (Cyclin D1 expression, p27Kip1, MTT,

interaction between MTT and p27Kip1), which will be discussed later. Significant results on

multivariate analysis suggested that tumour size is a predictor of DRR independent of the

above-‐mentioned factors. Despite the addition of these variables, the multivariate model

may not have been complete as there were other predictors identified in this review that

were not included, e.g. histological grade or perineural invasion.

Rodolico et al, 2004 (64) showed a significant association between tumour size and DRR.

The data was analysed and reported an OR with conditional binomial exact test

comparing the risk of recurrence to tumour size above and below 2 cm (T1 vs. T2 and T3).

This data was reanalysed as time to event in Rodolico et al, 2005 (7).(64) The results in

both of these reports may have been biased if larger tumours were more likely to receive

RTx so that RTx may have been a potential confounding variable in this analysis.

41

In summary, there are various points to consider when assessing tumour size as a

prognostic factor for recurrence. These include the treatment they received. Tumour size

was recorded as a continuous variable or a categorical variable with different cut-‐offs

used for tumour size. This leads to classification bias and lack of uniformity. Regarding the

cut-‐off, it was important how many patients in the study were above or below the cut-‐off.

It is also important whether they included both DRR and LR and only used recurrences

occurring after diagnosis and treatment.

In terms of local control, two studies provide conflicting evidence regarding tumour size

with Heller et al, 1979 (39) reporting a non-‐significant association and de Visscher et al,

2002 (57) reporting a significant association. In general, the event rates are low for local

recurrence, which suggests a need for a long period of follow up with a minimum of 2yrs

and a recommendation for at least 5 yrs of follow up.

It is recommended that future studies have uniform reporting practices, with analyses

being done for LR, DRR and local and regional control, with a minimum follow up of 5 yrs.

They should use a cut-‐off point of 2 cm as well as assessing tumour size continuously.

They should also document the event rate and any potential biases in selecting the

patients for analysis and should state how the patients were treated and assess the

treatment option as a potential confounding variable.

42

Histological grade

Table 8 Summary of results for histological grade

Study Cut point N HR 95%CI P value FUP Event rate

Zitsch et al, 1999 (5) G1 -‐ G4 1001 -‐ -‐ <0.0001 5 yrs 4%

Rodolico et al, 2005 (7) G1&G2 vs. G3&G4 97 3.51 (1.15-‐10.75) 0.028 5 yrs -‐

Rodolico et al, 2004 (64) G1&2 vs. G3&4 97 -‐ -‐ 0.016 5 yrs -‐

Rodolico et al, 1998 (65) G1&2 vs. G3&4 54 -‐ -‐ <0.05 -‐ -‐

n: Sample size, HR: HR 95%CI: 95% Confidence interval, FUP: Minimum follow up, Mets: Metastases, DRR: Delayed regional recurrence, Cat. : Categorical, *: multivariate model, G: Grade, all studies used DRR, yrs: years

Histological grade is a risk factor for predicting recurrence. Four studies analysing

histological grade as a risk factor for recurrence have been investigated.

Zitsch et al, 1999 (5) assessed histological grade as a predictor of regional recurrence. In

this study as the histological grade increased from grade I to IV, the proportion of patients

with DRR also increased. The proportion of patients with grade I tumours who developed

DRR was 2% whereas 20% of patients with grade IV tumours developed DRR (P <0.0001).

For histological grading the authors referenced a modified Broder’s grading system, which

is similar to the system proposed by Anneroth, as discussed earlier. The grading system

reported is important for standardisation, so that valid comparisons between studies can

be made. A significant increase of 8% to 20% nodal recurrence was noted between grade

III and grade IV histology. This is important for risk modelling purposes, as the increase in

recurrence associated with increasing tumour grade was not linear.

Rodolico et al, 2005 (7), in a Cox regression analysis, found grade to be a significant

predictor of DRR on univariate analysis. Since all patients in the study were treated by Sx,

histological specimens were available. The risk of DRR was significantly higher for patients

with grade III and IV compared to grade I or II (HR: 3.51, 95%CI 1.15-‐10.75, P=0.028).

43

Those with histologically confirmed metastases at presentation were excluded. This study

reported the Anneroth grading system. Histological grading was not present in the final

multivariate analysis, as it had been eliminated by backward regression. This suggests

predictors such as tumour size, MTT and molecular factors, which were included in the

final model, had a stronger association with DRR than histological grading and that

histological grade of the tumour was not an independent predictor of DRR.

Rodolico et al, 2004 (64) on a Chi-‐squared test (univariate) found histological grade to be

a significant predictor of DRR. Histological grading was reported according to the

Anneroth system. The authors noted a significant difference between grade III and IV vs.

grade I and II (P=0.016). Note, that this is the same data as Rodolico et al, 2005 (7) but

analysed as occurrence of event rather than time to event.

Daniele et al, 1998 (66) found histological grade to be a predictor of DRR. The study

compared grade III and grade IV vs. grade I and grade II. This was a smaller study of only

54 patients and all under went Sx with histological specimens reported on all. The high

proportion of DRR in patients with grade I tumours is misleading because there were only

4 patients with grade I tumours (1 recurrence out of 4 grade I tumours).

In summary all four studies have shown that the histological grade of the tumour may be

an independent risk factor for predicting recurrence. These results have been found using

both the Broder’s and Anneroth grading system. However, these studies may still be

underpowered with even the largest study (5) having only a 4% event rate. Also these

studies did not adjust for other confounding variables, especially tumour size.

44

Maximal tumour thickness

Table 9 Summary of results for maximal tumour thickness

Study Outcome

Cut-‐off

point N Effect size 95%CI P value FUP Event rate

de Visscher et al, 1998 (62) LR -‐ 184 -‐ -‐ >0.05 2 yrs 6%

de Visscher et al, 1998 (62) DRR -‐ 184 -‐ -‐ <0.001 2 yrs 6%

Rodolico et al, 2005 (7) DRR -‐ 184 -‐ -‐ <0.001 2 yrs 6%

Rodolico et al, 2005 (7) DRR 5mm 97 13.17 HR (3.61-‐48.02) <0.001 5 yrs -‐

Rodolico et al, 2005 (7) DRR cont. 97 1.32 HR (1.18-‐1.48) <0.0001 5 yrs -‐

Rodolico et al, 2005* (7) DRR 5mm 97 10.93 HR (2.11-‐56.44) 0.043 5 yrs -‐

Rodolico et al, 2005** (7) DRR cont. 97 0.96 HR (0.74-‐1.25) 0.072 5 yrs -‐

Rodolico et al, 2004 (64) DRR 5mm 97 8.03 OR (2.76-‐25.04) <0.0001 5 yrs -‐

n: Sample size, 95%CI: 95% Confidence interval, FUP: Minimum follow up, LR: Local recurrence DRR: Delayed regional recurrence, *:Multivariate analysis, **: Multivariate analysis with interaction term

MTT, sometimes referred to as depth of invasion, is defined as the distance from the

granular layer through to the thickest portion of the lesion measured in mm.(54) MTT is

also defined as the distance measured vertically from the surface of tumour to the base

of the tumour. This excludes surface layers of parakeratotic cells and inflammatory

exudates, and is measured using an ocular meter.(6)

As tumours grow they invade into surrounding structures including microvessels and via

these may enter the blood stream as they increase in thickness and invade deeper. Hence

MTT may be a risk factor for recurrence if escaping micrometastases establish in nodal or

distant sites. Also another hypothesis is that tumours with increasing thickness may

become more aggressive biologically.

45

De Visscher et al, 1998 (62) reported no significant association between MTT and LR but

did report a statistically significant association with DRR (P<0.001). An MTT cut-‐off point

was not documented, but the association referred to increasing MTT and DRR. There

were 184 patients in the study with an event rate of 6%.

Rodolico et al, 2005 (7) modelled MTT as both a continuous and categorical variable

univariately and multivariately in a Cox model. Using time to DRR the study found that

MTT was significant on univariate analysis in both continuous and dichotomous (cut-‐off

5mm) formats (see Table 9). MTT on multivariate analysis was not significant as a

continuous variable and was likely to be crowded out by other factors such as the

included interaction variable between MTT and p27Kip1 LI, which is a tumour suppressor

gene (p27Kip1 LI is mentioned later). This interaction term was highly significant

(P=0.0053). Analyses may be difficult to interpret when the individual term is non-‐

significant but the interaction term is significant. MTT when dichotomised, was significant

on multivariate analysis (MTT cut-‐off 5mm) (HR:10.93 95%CI: 2.11-‐56.44;P=0.043). This

suggests that the interaction term has taken up much of the variability in the analysis

where MTT is continuous as it was not present in the multivariate categorical analysis.

This interaction term suggests that patients who have a p27 Kip1 LI expression of <20%

have a decreased risk of DRR as does an MTT >5mm, but furthermore, both these

features combine to result in an increasing risk of recurrence. This suggests that the

combined risk is more than the sum of the component risks. This is likely due to some

underlying mechanism where the effect of a high MTT is amplified by low p27 Kip1 LI.

Rodolico et al, 2004 (64) reanalysed the same data as Rodolico et al, 2005 (7) but looked

at event occurrence instead of time to event and found MTT to be a significant predictor

of DRR with an OR of 8.03. An inverse correlation between MTT and p27Kip1LI was seen,

where MTT rose with decreasing expression ofp27Kip1LI.(64) Mean MTT in group 1 (no

46

recurrence) was 4.2 mm (range 1.2-‐12.1) and group2 (recurrence) 11.2 mm (range 4.1 -‐

16.7). This result suggests that a cut-‐off point MTT of 4-‐5 mm may be acceptable for

separating risk groups.

Daniele et al, 1998 (66) found MTT to be a significant predictor of DRR as an event rather

than time to event using an OR determined from a Chi-‐squared test. The study used 6 mm

as the cut-‐off for MTT and reported a significant P value (P<0.001). This adds further

supportive evidence to the argument that increasing MTT is a predictor of regional

recurrence.

Frierson et al, 1986 (6) calculated the mean MTT of two groups based on DRR. Group 1

had 157 patients with no DRR while group 2 had 30 patients that experienced DRR. The

mean MTT for group 1 was 2.5 mm (range:0.5-‐16.8mm) compared with 7.5 mm in group

2 (range: 2.2 – 16.0mm), P<0.001. Therefore, a cut-‐off of 6 mm for MTT is reasonable as it

lies between the two means. The summary of these studies provides supportive evidence

of MTT as a predictor of DRR for both the event and time to event.

In summary a number of studies have analysed the association of increasing MTT and

recurrence using different methodology. The association was confirmed as significant on

both univariate and multivariate analyses. However, studies have not shown MTT to be a

risk factor of recurrence independent of tumour size.

47

Site of lip cancer

Table 10 Summary of results for site of lip cancer

Study Cut point N OR 95%CI P value FUP Event rate

Zitsch et al, 1999 (5) Upper, lower and commissure 1001

>0.34 5 yrs 4%

Fernandez et al, 2003 (61) Commissure and lower lip 251 11.06 (3.17-‐38.59) <0.001 5 yrs 6.40%

n: Sample size, OR: Odds ratio, 95%CI: 95% Confidence interval, FUP: Minimum follow up,

all studies reported delayed regional recurrence

Lip cancer can either arise on the lower lip, upper lip or the commissure (junction of the

upper and lower lip), with the majority (95%) of lip cancers arising on the lower lip and

the remaining arising on the upper lip and commissure. The aetiology of these different

subsites is suspected to be also different as the lower lip is predominantly exposed to the

sun. Some studies have associated a poorer prognosis with non-‐lower lip cancers and

suggested these as representing a site of more aggressive disease with possibly different

risk factors.

Zitsch et al, 1999 (5) reported no association between site of lip cancer (i.e. lower lip vs.

upper lip vs. commissure) and DRR (P>0.34). The authors reported a 4% rate of DRR in the

lower lip and a 6% rate of DRR in the other two subsites. Of note, 95% of the study

constituted lower lip cancer, with only a small minority having non-‐lower lip cancers.

Fernandez et al, 2003 (61) analysed DRR in patients with lip cancer. There was a 10 yr

collection period and minimum follow-‐up of 5 yrs. They found commissural localisation to

have a significant OR of 11.06 in predicting lymph node metastases. Localisation to

commissure only included tumours on the commissure. This is important because some

studies define lip cancer on the commissure as tumours that have commissural

48

involvement, even if they developed on the upper or lower lip. Commissural tumours

arising from the lower or upper lip are often larger tumours, which are more likely to

recur. Also commissural involvement alone does not answer the hypothesis of whether

tumours originating from the commissure have an increased risk of DRR.

In summary, Zitsch et al, 1999 (5) and Fernandez et al, 2003 (61) have varying views that

tumours covering more than one site are more likely to recur regionally. Fernandez et al,

2003 (61) confirms tumours presenting only in the commissure are of higher risk of DRR.

Cellular and molecular factors

Table 11 Summary of results for cellular and molecular factors

Variable Study Cut point N Effect size 95%CI P value FUP

p27Kip1 Rodolico et al, 2005 (7) Continuous 97 0.92HR (0.89-‐0.96) <0.0001 5 yrs

Rodolico et al, 2005 (7) Low vs. High (by median) 97 9.37HR -‐ <0.0001 5 yrs

Rodolico et al, 2005* (7) Continuous 97 0.70HR (0.57-‐0.88) 0.002 5 yrs

Rodolico et al, 2005* (7) Low vs. High (by median) 97 2.28HR (0.47-‐11.09) 0.069 5 yrs

Rodolico et al, 2004* (64) Low vs. High (by median) 97 20.48OR (5.40-‐80.33) <0.0001 5 yrs

CyclinD1 Rodolico et al, 2005 (7) Pos. or Neg. 97 7.94HR (2.18-‐28.9) 0.002 5 yrs

Rodolico et al, 2005* Pos. or Neg. 97 13.13HR (1.77-‐91.89) 0.02 5 yrs

Rodolico et al, 2005* (7) Pos. or Neg. 97 4.83HR (0.99-‐23.37) 0.02 5 yrs

DNA aneuploidy Daniele et al, 1998 (66) Aneuploid vs. Diploid 54 -‐ -‐ <0.001 -‐

PCNA Daniele et al, 1998 (66) LI=0.48 54 -‐ -‐ <0.001 -‐


HR: Hazards ratio, OR: Odds ratio, *: multivariate model, all studies reported delayed

regional recurrence

49

The cellular and molecular factors investigated as potential risk factors for recurrence

were p27Kip1, Cyclin D1, DNA aneuploidy and proliferating cell nuclear antigen (PCNA).

There are few studies investigating these factors in terms of predicting recurrence. If

there is an independent association the expression of these molecular factors could

provide further prognostic information that may potentially impact on management.

p27Kip1

p27Kip1 is a tumour suppressor gene. Its expression is detected immunohistochemically

using the monoclonal antibody K25020.(64) p27Kip1 expression is documented in 2 studies

(Rodolico et al, 2005 (7) and Rodolico et al, 2004 (64)) as a predictor of DRR. Both studies

found an interaction with p27Kip1 and MTT and analysed the same dataset. Rodolico et al,

2005 (7) looked at time to event and Rodolico et al, 2004 (64) looked at event occurrence,

where the event was DRR.

The median expression of p27Kip1 was found to be 19.7% and at this cut-‐off the OR for

significantly predicting DRR was 20.48 (95%CI: 5.40-‐80.33; P<0.0001).(64) The estimated

HR when predicting time to event using a cut-‐off of 20% expression was 9.37 (P<0.0001).

This was on univariate analysis with p27Kip1 expression as a categorical predictor. The

univariate HR for p27Kip1 expression as a continuous variable was significant with HR:

0.92(95%CI: 0.89-‐0.96; P<0.0001).(7) This HR is interpreted as a fall in risk by 8% for an

increase of 1 percent in p27Kip1 expression.

On multivariate analysis, p27Kip1 expression as a continuous variable had a significant HR:

0.70 (95%CI: 0.57-‐0.88; P=0.002) when adjusted for tumour size, cyclic D1 expression,

MTT and MTT interaction with p27Kip1 expression. p27Kip1 in a categorical multivariate

analysis had a borderline significant HR of 2.28 (95%: 0.47-‐11.09; P=0.069) when adjusted

for the same variables. The HR of 2.28 describes an increased risk associated with low

expression of p27Kip1. On dichotomisation there is a loss of information, which explains

50

the shift from highly significant to that of borderline significance. Of note in these

analyses for categorical analysis a low p27Kip1 is of interest and a high p27Kip1 is considered

the baseline, whereas on continuous analyses the HR refers to the fall in risk for 1%

increase in p27Kip1 expression.

The increase in HR from univariate to multivariate suggests that even after adjusting for

key variables such as MTT and tumour size, p27Kip1 expression is significant. This result

highlights the key role of p27Kip1 in predicting DRR independently of other key factors

(MTT and tumour size).

Cyclin D1

Cyclin D1 is a proto-‐oncogene whose expression is upregulated in tumour development.

The implication of analysing this variable has only been reported in one study (Rodolico et

al, 2005 (7)). Cyclin D1 expression was classified by the intensity of nuclear staining within

tumour cells and the percentage that were positive.(7) Cyclin D1 was assessed

categorically in univariate and multivariate analyses, whereas in the multivariate analysis

there were two models; one adjusted for continuous covariates and the other adjusted

for categorical covariates.

Univariately the HR was significant at 7.94 (95%CI: 2.18-‐28.9; P=0.002), which indicates a

substantial effect. The results of the multivariate models vary greatly depending on

whether the remaining covariates were categorically or continuously coded. Where the

remaining variables were continuously coded, the HR for Cyclin D1 expression was

significant at 13.13 (95%CI: 1.77-‐91.89; P=0.02) and where they were categorically coded,

the HR was also significant at 4.83 (95%CI: 0.99-‐23.37; P=0.02). Even though the P values

were equal, the loss of information from continuous to categorical classification of the

remaining covariates warrants using the model where confounding variables were

51

treated as continuous variables. Therefore the higher HR of 13.13 may represent the true

effect size.

DNA aneuploidy

DNA ploidy is measured by comparing the ratio of DNA content of the tumour cell peak to

a control (DNA index (DI)). Diploidy is normal when DI<1.25, periploidy is defined as

1.25<DI<1.4, and aneuploidy as DI>1.4. DNA aneuploidy is more frequently reported in

poorly differentiated tumours and thus associated with the histological grade of a

tumour.(66)

Daniele et al, 1998 (66) reported on DNA aneuploidy, with the OR significant for

predicting DRR. However, there are some points of contention in this study. Firstly,

tumours coded as periploid were not shown as the total sample size for calculating a P

value of diploid and aneuploidy add to the total sample size of study. Therefore it is likely

that periploid tumours may have been coded as diploid although no details were given.

Although the sample size is small with 2 cells of the two-‐by-‐two table (2x2 table)

containing 5 or fewer patients, the event rate in the aneuploidy group is considerably

higher. Aneuploidy may be another important histological variable. However, the impact

of this variable is unknown, as there were no multivariate models that included

histological grade to confirm whether the level of DNA ploidy is an independent

determinant of outcome in lip cancer.

PCNA

PCNA is detected immunohistochemically using PC10 antibody. PCNA expression is

associated with a rapid tumour growth rate and is associated with poor prognosis in other

malignant tumours, such as breast cancer, colorectal cancer and oral cancer.(66) The

association in Daniele et al, 1998 was significant (P<0.05), with the event rate being much

52

higher in patients expressing high levels of PCNA.(66) The association was for predicting

DRR.

In conclusion, these 4 cellular/molecular factors may be predictors of DRR. However,

further studies are required to establish the role of molecular variables in predicting

outcomes for patients with lip SCC. Investigating whether these factors predict recurrence

independent of other established risk factors such as tumour size, MTT or histological

grade would be particularly useful. Currently these variables play no role in clinical

management.

Perineural invasion

Table 12 Summary of results for perineural invasion

Study Outcome Cut point N HR 95%CI P value FUP

Frierson et al, 1986 (6) RR at presentation Absent/ Present 186 -‐ -‐ <0.001 -‐

Rodolico et al, 2005 (7) DRR Absent/ Present 97 9.78 (2.64-‐36.27) <0.001 5 yrs

Rodolico et al, 2004 (64) DRR Absent/ Present 97 -‐ -‐ 0.008 5 yrs

n: Sample size, 95%CI: 95% Confidence interval, HR: Hazard ratio, FUP: Minimum follow

up, DRR: Delayed regional recurrence, RR: Regional recurrence

Perineural invasion (PNI) is the invasion of tumour cells into the perineural space.(6) Four

studies have analysed PNI as a predictor of DRR. PNI is reported in only a minority of

patients and is an indicator of tumour invasion, increasing the likelihood of recurrence.

Frierson et al, 1986 (6) identified the presence of PNI in 5% of patients that did not

experience DRR, in comparison PNI was detected in 41% of patients who experienced

53

DRR, which was a significant difference (P<0.001) with no OR given. In this study 11% of

patients overall had PNI, in keeping with only 10-‐15% of patients having this reported in

other non-‐lip cutaneous SCC.(6)

Daniele et al, 1998 (66) reported a non-‐significant association between PNI and DRR, but

this was solely due to the fact that there was a zero cell (there were no patients with PNI

that were recurrence free) so that a P value was unable to be calculated. The analysis may

have yielded significant results associating PNI with DRR if the authors had implemented a

suitable zero-‐cell methodology. Since this study did not use a suitable zero-‐cell

methodology the non-‐significant association may not be correct and the association

therefore remains untested. This is why the study was not included in Table 12.

Rodolico et al, 2004 (64) and Rodolico et al, 2005 (7) commented on PNI using the same

dataset, one using the event and the other using time to event, respectively. Rodolico et

al, 2004 (64) analysed predictors of the occurrence of the event (DRR), and found a

significant association of PNI to the event. Similar to Daniele et al, 1998 (66) there was

only 1 patient with PNI who was recurrence free. The definition of PNI and its assessment

methods were not given.

Rodolico et al, 2005 (7) looked at the data from a time to event perspective to obtain a

HR. PNI was only assessed in a univariate model and reported a significant HR: 9.78

(95%CI: 2.64-‐36.27; P<0.001). This study had 97 patients with a minimum follow up of 5

yrs. Here the cut-‐off for the variable was the presence or absence of PNI.

The evidence suggests that patients with PNI are more likely to develop recurrence than

patients without PNI. However, these results were obtained from only 4 studies, where

the incidence of PNI is only 10-‐15%. Larger, better-‐constructed studies are required

54

before the evidence is conclusive. Due to the low number of patients with PNI who

remained recurrence free (e.g. Daniele et al, 1998 (66) and Rodolico et al, 2004 (64)),

studies should be constructed as a time to event with the objective of obtaining a HR via

survival analysis.

Other risk factors

Table 13 Summary of results for ulcerated pattern and tumour area

Variable Study Cut Point n OR 95%CI P value FUP Event rate

Ulcerated pattern Zitsch et al, 1999 (5) Absent/ Present 1001 -‐ -‐ <0.05 5 yrs 4%

Tumour area Zitsch et al, 1999 (5) Absent/ Present 1001 1.17 (1.03-‐1.32) -‐ 5 yrs 4%


all studies reported delayed regional recurrence

Lip cancer growing in an ulcerated pattern at presentation was identified by Zitsch et al,

1999 (5) as a significant predictor (P<0.05) for DRR as seen in Table 13, with no patients

experiencing DRR that had non-‐ulcerated lip cancer in this study. This was a large study

with 1001 patients and 5 yrs minimum follow up however the event rate for DRR was only

4%. This suggests even with the large sample size the study may have been

underpowered.

In Zitsch et al, 1999 (5) tumour area in a multivariate model with age and localisation had

an association with DRR. The mean tumour area was measured and compared between

recurring and non-‐recurring patients, with an OR of 1.17. This may have been a

statistically significant result, as the OR of 1 was not contained within the confidence

interval. It would be expected that if tumour size, MTT and the extent of invasion are

associated to DRR, so too should tumour area.

55

Muscle invasion and vascular invasion are also worth noting. Muscle invasion was defined

as tumour infiltrating muscle 1 mm or more as measured by an ocular micrometer in

Frierson et al, 1986.(6) 40% of group 1 (patients with non-‐metastasizing carcinomas at

evaluation) had muscle invasion, whereas in group 2 (patients with metastasizing

carcinomas at evaluation), 77% had muscle invasion.(6) An analysis of the data was not

done so P values and OR were not quoted. The authors found vascular invasion difficult to

assess due to inflammation and fibrosis.

Survival and its risk factors

The 5-‐year relative survival for SCC of the lip was 90.9% in NSW from 1999-‐2003 using the

Cancer Council data.(10) Relative survival compares survival of patients with the general

population of the same age group; hence, it is usually higher than OS, which does not take

age into consideration. The 5-‐year CSS statistic from the National Cancer Database in the

USA was 91.1% after following 10,274 patients for that period. This data was collected by

the SEER Program, and 85.2% of these patients underwent Sx alone, so RTx as a

treatment was under-‐represented. Therefore this survival outcome may not reflect the

patient population treated with lip cancer. The reported OS is less than CSS in lip cancer

as the majority of patients tend to die from other unrelated causes. The correct method

to calculate survival as mentioned is to ideally use a competing risks survival analysis.

Zitsch et al, 1995 (2) investigated whether variables other than treatment affected

survival. This study categorised predictor variables and compared the determinate or CSS

for each category. This is presented in Table 14.(2) This was a large study with 1047

patients and a recruitment period from 1940 to 1987. However, this study only recorded

75 deaths (7.2%) due to lip cancer.

In this article(2), the use of CSS as an outcome was problematic. CSS is potentially biased

by the number of deaths not due to lip cancer as these patients are censored. Uneven

56

distribution of these deaths could cause uneven censoring, which in turn could lead to

bias. For example, if a certain risk factor causes patients to die for a reason other than lip

cancer, then although OS rates are worse, the CSS would improve.

In the CSS column the denominator is the number of patients in that group (e.g. number

of males in the study). The numerator is the number of patients that were alive at the end

of the 5-‐yr follow up.

The article reports that as tumour size or grade rises, there is a decrease in CSS. Regional

or distant metastases also lead to a lower CSS. This is valid as larger tumour size and

increasing tumour grade are associated with DRR, which is itself associated with higher

mortality. Site, which was a contentious variable in predicting DRR, is a predictor of

survival in this study. This may confirm that sites other than the lower lip have a more

aggressive disease. Gender had a weak association but this could be due to the small

sample size and the small proportion of women in the study.

57

Table 14 Risk factors predicting 5 yr-‐cause specific survival in lip cancer in one study

Patient variable CSS P value

Tumour size

<0.0015

<1 cm 221/229

<2 cm 299/330

<3 cm 78/92

<4 cm 22/33

>4 cm 29/47

Tumour grade

<0.01

I 429/455

II 85/101

III 11/19

IV 25/35

Adenopathy/Metastases 76/124 <0.001

Radiation therapy 226/260 <0.001

Surgical margins clear 398/424

<0.024

Site

0.043

Upper lip or commissure 20/26

Lower lip 608/676

Gender

0.112

Male 652/732

Female 17/22

Table courtesy of Zitsch et al, 1995 (2)

58

Analysis of the Westmead lip cancer dataset

This section details the methods, analysis and interpretation of results on patients with

SCC of the lip. This is a study of 27 years duration comprising of both retrospective and

prospective data. This dataset recorded, among many variables, time to death and time

to recurrence of disease from the date of diagnosis.

Our study investigated risk factors that are potentially associated with time to recurrence

of disease and survival, including whether different treatments altered the risk of the

outcome. This study was motivated by the need to determine the predictors and

prognostic factors for determining high risk patients in terms of time to recurrence and

survival, so that they may potentially be identified and managed accordingly.

Materials and methods

A retrospective dataset of patients diagnosed with lip SCC treated between January 1980

and July 2007 at Westmead Hospital was analysed. Data collected before December 1997

was retrospective, whilst data collected after that date was prospective in the sense that

patients were recorded and followed up prospectively. All patients were added to the

study after assessment in a multidisciplinary head and neck clinic.

Patient eligibility

Patient records were examined and if all the inclusion criteria were met, they were

considered eligible for the study. Additionally if any of the exclusion criteria were satisfied

then the patient was removed from the study cohort.

59

Inclusion criteria

• Patients who presented to Westmead Hospital in the study period between

January 1980 and July 2007.

• Patients who were diagnosed with biopsy proven SCC of the lip.

• Patients who were treated at Westmead with either Sx, RTx or Sx+RTx.

Exclusion criteria

• Patients whose follow-‐up was less than 6 months, unless they died or relapsed

within the time period of 6 months, in which case patients were included in the

survival dataset.

• Patients dying before 6 months who had not relapsed were excluded from the

relapse dataset.

• Patients previously treated elsewhere before presenting to the Westmead

Hospital.

Treatment

Treatment delivered was either RTx or Sx or a combination of these. If excision margins

were considered inadequate (e.g. close or positive margins) then a recommendation of

adjuvant local RTx (Sx+RTx) was often made. Clinicians in a multidisciplinary head and

neck clinic assigned treatment options.

Methods

All collected data was recorded on a data collection form and entered into a computer

database (SPSS). The final database was cleaned and codified in a manner appropriate for

statistical analysis. Extreme or illogical values were checked and where necessary

resolved by going back to the patient’s file or using other sources of information, such as

60

electronic patient databases. Illogical values that needed to be checked were tumour size

in a handful of patients (less than 10 patients) where the tumour size status (T score) did

not match the tumour size in mm. These values were checked with the patient file and

confirmed by my supervisor to ascertain the correct value.

Statistical analyses carried out in this chapter are divided into 4 sections. The first section

is the analysis of baseline demographics, which includes relevant cross tabulations of the

variables in the dataset concerning treatment and outcome. These include tumour size,

laterality, location, histological grading, gender, age and smoking status.

The second section relates to univariate survival analyses of predictors for survival and

recurrence. The third section looks at the adjusted effect of treatment with additional

statistically significant variables included as confounding variables. The addition of such

confounding variables needed to be significant with the inclusion of the treatment

comparison. The fourth section looks at risk profile modelling with survival and

recurrence as outcomes. Separate survival risk models were constructed with and

without treatment, where sufficient predictors were available.

Methods of univariate analysis

To my knowledge, the only other study employing time to event methods was Rodolico et

al, 2005 (7). This study however, only considered the association of baseline predictors on

time to event outcomes and did not consider any treatment comparisons. My approach

takes into account the order of events (using proportional hazard models) and thus is a

potentially more powerful method than some of the standard methods used in the

literature that model the OR. Also survival analysis accounts for censored observations in

a systematic fashion, whilst modeling the OR only accounts for non-‐events as a

proportion at the end of the study. This adds to the power depending on the

characteristics of the dataset.

61

In our study 95/190 patients were censored within 5 years in the survival dataset and

99/191 in the recurrence dataset, with regards to death and recurrence as an outcome

respectively. Patients who died and did not recur were considered as censored in the

recurrence dataset.

Proportional hazards model examines the risk of an event among groups of patients

determined by the categories of the variables being considered for analysis. Associations

between exposures and the outcome risk are usually expressed as HRs.

Univariate models were performed to test the statistical significance of the predictors and

treatment comparisons in estimating the strength of any association between these

variables and the outcome. This can be obtained by examining whether the HRs were

statistically different from unity for each predictor. A univariate analysis aids in the

selection of candidate variables into a multivariate model (whether or not the variable is

eventually statistically significant). If a variable was significant on a univariate analysis but

not significant in a multivariate analysis then the perceived association observed can be

better explained by other variables, which appear in the model.

For those variables that were statistically significant and comprised of only two groups,

plots showing the cumulative proportion experiencing the event were constructed to help

visualise survival differences. These are Kaplan Meier (KM) plots and were constructed

according to the guidelines set out by Pocock et al, 2002.(67) The plots are curtailed at 5yr

follow-‐up to avoid misleading inferences based upon the tail of the curves, where there

are fewer patients still being followed up in the cohort. Also 5yr recurrence/survival rates

are useful indicators of treatment benefit in this disease. These cumulative incidence

plots allow for visualisation of the differences between curves when the event rate is low,

because they are increasing and the area of the plot above the curves can be minimised.

The log-‐rank test was performed on all the dichotomous predictors in the univariate

62

analysis to test for differences between the survival curves. A HR, 95% CI and a P value

were calculated to measure the effect size of a variable. Finally at the base of the curve

(below the x-‐axis), the 'Number at risk' table has been included to provide the number of

patients contributing information at each time point.

Methods for adjusted treatment effect

In order to determine whether any treatment effect is sustained after accounting for

different confounding variables, adjusted treatment effect models were examined. This

was done for each of the four treatments comparisons, with two corresponding models,

one for survival and the other for recurrence. Adjusted treatment effect models include

statistically significant confounding variables. Confounding variables are related to both

the outcome (survival or recurrence) and the predictors.

Methods of risk models

Three risk models were developed with the aim of understanding disease progression

with treatment by classifying patients into risk groups using risk models based on the

impact of various predictors, prognostic factors and treatment comparisons. Two of the

risk models use survival as an outcome, one with a treatment term (i.e. whether or not

they received Sx or Sx+RTx) and the other without a treatment term. The models without

treatment were developed to better understand the impact of significant baseline

predictors on disease progression regardless of any subsequent treatment, whereas the

model including the treatment comparison was for understanding the contribution of a

specific treatment on disease progression. This was repeated for the outcome of time to

recurrence.

Patients with SCC of the lip treated at this institution (Westmead Cancer Care Centre)

may be more likely to have more advanced disease (higher risk of recurrence and poor

63

survival) as this is a tertiary referral centre. This implies there may be patient selection

bias and institutional bias in these results.

The variables included in a risk model are usually those that are significantly associated

with the outcome. However, variables that trend towards significance may also be added.

The treatment comparison was limited to patients treated with Sx or Sx+RTx vs. RTx as all

patients underwent one of these treatments. For identifying risk, a prediction score using

the coefficients from the corresponding Cox Proportional Hazards model was obtained for

each patient. The median value of this prediction score was then selected as the cut-‐off

point and this was used to classify patients into high risk (those with a prediction score

above the cut-‐off point) and low risk (those with a prediction score below the cut-‐off

point). Curves showing the cumulative proportion experiencing the event were

constructed to illustrate the risk models based on the cut-‐off point.

The risk indicator variable was constructed whereby patients classified as high risk were

assigned a 1 and low risk were assigned a 0. A cross tabulation of the risk grouping and

the event of death or being alive was created to provide an understanding of how well

this classification predicted outcome. The strength of this classification can be measured

by OR. This was repeated for recurrence (yes/no).

In order to test the adequacy of my modelling two approaches were used the Gronnesby

Borgan (GB) goodness of fit test, 1996 (68) and the May and Hosmer (MH), 1998 (69)

approach. Asymptotically (i.e. with a large number of events) these tests are equivalent,

but because my dataset did not contain many events both approaches were reported.

The GB goodness of fit test compares the observed number of events in each group with

the expected number of events (obtained from martingale residuals which is a complex

mathematical formula). When the number of observed events is close to the expected

number of events the test is non-‐significant suggesting the model is a good fit.(70)

64

The MH approach adds the risk indicator variable as a covariate in the proportional

hazards model and tests for its significance. If this risk indicator variable is not significant

than this suggests the original proportional hazards model is an adequate fit.

Typically risk score equations comprise of coefficients, which have a number of significant

digits. It is then helpful to construct a profile index (PI), which simplifies the weights

attributed to each of the variables in the prediction model. This allows for easier

understanding and use by clinicians.

Dataset description

Two outcomes (survival and recurrence), four different treatment comparison (see Table

15), 11 predictors (Table 16) were considered and yielded three risk categorisation

indicator variables for the three risk models. Recurrence was defined as either local (at

the primary site) alone or regional (lymph nodes). Survival and recurrence were chosen,

as they are the important time points in the progression of any treated cancer.

Patients underwent either Sx, or RTx or Sx+RTx. The comparison of Sx alone vs. the other

treatments was not performed because those patients receiving Sx+RTx were principally

surgical candidates and combining them with the RTx group would produce a group of

heterogeneous patients making interpretation difficult.

65

The four treatment groups devised were as follows:

Table 15 Treatment definitions

Treatment Variables Definition Excludes patient group who received:

Sx vs. RTx Surgery alone vs. Radiotherapy alone Adjuvant Radiotherapy

Sx or Sx+RTx vs. RTx Any surgery vs. Radiotherapy alone None

Sx+RTx vs. Sx Surgery with adjuvant Radiotherapy vs. Surgery alone Radiotherapy alone

Sx+RTx vs. RTx Surgery with adjuvant Radiotherapy vs. Radiotherapy alone Surgery alone

Ten variables were collected that were relevant to all groups and had near complete

information. Some variables only relate to the Sx group as they described tumour

characteristics following excision, e.g., MTT, which was impossible to measure in patients

undergoing RTx. Only three of the variables selected had missing values and the number

of missing values were tumour size in mm (14/190), well differentiated (36/190) and

smoking status (17/190). The remaining variables had complete information.

Among those variables considered, tumour size has been extensively investigated in the

literature and 5 out of 7 studies reported a significant association with recurrence where

larger tumours predicted for recurrence.(3, 7, 39, 57, 61, 63, 64) The laterality of lip SCC

as a potential predictor of recurrence or survival was not reported by any study. The site

of the lip cancer (upper vs. lower vs. commissural) has been investigated in one study.(61)

Histological grade has been investigated in 4 studies and all showed a significant

association with recurrence.(7, 61, 64, 65) Age was investigated in two studies, which

reported no association with recurrence.(5, 61) Smoking has not been investigated with

respect to recurrence, but pipe smoking is a risk factor for developing lip cancer in a

subset of patients. The relative risk of developing lip cancer was higher in pipe smokers

then smokers of other forms of tobacco.(12) Gender was investigated in 4 studies, which

showed no association between gender and DRR, or tumour size, or CSS for lip cancer

patients.(2, 5, 20, 62) Therefore we have investigated the following 11 variables.

66

Table 16 Patient and tumour predictor definitions

Predictor variables Definition

T1 Tumour smaller than 20 mm in longest dimension

≥T2 Tumour equal to or larger than 20 mm in longest dimension

Left side Tumour on the left vs. not left side.

Size Size of the tumour (histological size in Sx patients and clinical size in RTx patients)

Size≥20mm Size greater than or equal to 20 mm (binary variable)

Lower lip Tumour on lower lip vs. (upper lip or commissure)

Well diff Well differentiated vs. (moderately or poorly differentiated)

Gender Gender of patient

Age Age at first diagnosis in years

Age≥70 Age at diagnosis dichotomised at 70 years

Smoker Smoker or ex-‐smoker vs. never smoked

67

Results

Baseline demographics

The baseline demographics were tabulated according to continuous and dichotomous

predictors for the survival and recurrence models using preliminary treatment and

outcome groupings.

Table 17 Summary measures on age of patients by treatment groups

Age at diagnosis (years) N Mean St. Dev. Median Min Max

Sx 98 58.688 17.096 60.242 17.483 88.578

RTx 85 63.498 16.37 63.775 27.367 97.314

Sx + RTx 24 64.88 16.587 69.283 29.931 86.844

Overall 207 61.405 16.851 62.981 17.483 97.314

St. Dev.: Standard deviation, Sx+RTx: Surgery and adjuvant radiotherapy

68

Dichotomous variables used in overall survival modelling

Table 18 Baseline dichotomised variables and all cause mortality

Variable Alive (n=142) Sx (n=89) RTx (n=79) Sx+RTx (n=22) Total (n=190)

Male 106 (75%) 65 (46%) 63 (44%) 14 (10%) 142

Female 36 (75%) 24 (50%) 16 (33%) 8 (17%) 48

T1 114 (79%) 77 (53%) 48 (33%) 19 (13%) 144

≥T2 28 (61%) 12 (26%) 31 (67%) 3 (7%) 46

Lower lip 124 (74%) 78 (47%) 72 (43%) 17 (10%) 167

Upper lip or Commissure 18 (78%) 11 (48%) 7 (30%) 5 (22%) 23

Well diff 60 (78%) 37 (48%) 30 (39%) 10 (13%) 77

Moderately diff 53 (82%) 37 (57%) 21 (32%) 7 (11%) 65

Poorly diff 8 (75%) 5 (42%) 2 (17%) 5 (42%) 12

Unknown 21 (58%) 10 (28%) 26 (72%) 0 (0%) 36

Left Side 46 (79%) 29 (50%) 23 (40%) 6 (10%) 58

Right Side or Midline 96 (73%) 60 (45%) 56 (42%) 16 (12%) 132

Smoker (ex or current) 86 (77%) 51 (46%) 48 (43%) 13 (12%) 112

Never smoked 46 (75%) 31 (51%) 23 (38%) 7 (11%) 61

Missing 10 (59%) 7 (41%) 8 (47%) 2 (12%) 17

Age<70yrs 97 (81%) 61 (51%) 48 (40%) 11 (9%) 120

Age≥70yrs 45 (64%) 28 (40%) 31 (44%) 11 (16%) 70

Survival risk model including treatment

High risk 50 (63%) 36 (46%) 31 (39%) 12 (15%) 79

Low risk 92 (83%) 53 (48%) 48 (43%) 10 (9%) 111

Survival risk model excluding treatment

High risk 65 (66%) 36 (37%) 50 (51%) 12 (12%) 98

Low risk 77 (84%) 53 (58%) 29 (32%) 10 (11%) 92

Recurrence risk model including treatment

High risk 81 (74%) 89 (81%) 0 (0%) 21 (19%) 110

Low risk 61 (77%) 0 (0%) 79 (100%) 0 (0%) 79

Diff: Differentiated (used in histological grading), Data not available for all variables, 1 patient excluded from recurrence modelling as they died within 6 months, before recurring.

69

Univariate models

Survival models from diagnosis

Table 19 Univariate results for overall survival

Survival from diagnosis to death

Variable HR 95%CI P value N failures (%n)

Age in years 1.04 (1.019 -‐ 1.061) <0.001 190 48 (25%)

Age≥70 years 2.849 (1.566 -‐ 5.181) 0.001 190 48 (25%)

T1 tumour 0.471 (0.26 -‐ 0.853) 0.013 190 48 (25%)

Left side 0.723 (0.372 -‐ 1.402) 0.336 190 48 (25%)

Smoker (ex or current) 0.832 (0.437 -‐ 1.581) 0.574 173 41 (24%)

Male gender 0.905 (0.457 -‐ 1.792) 0.775 190 48 (25%)

Lower lip 0.897 (0.352 -‐ 2.284) 0.82 190 48 (25%)

Well diff 1.067 (0.532 -‐ 2.14) 0.856 154 33 (21%)

Treatment HR 95%CI P value N failures (%n)

Sx vs. RTx 1.435 (0.752 -‐ 2.738) 0.274 168 40 (24%)

Sx or Sx+RTx vs. RTx 1.557 (0.847 -‐ 2.862) 0.154 190 48 (25%)

Sx+RTx vs. Sx 1.415 (0.625 -‐ 3.205) 0.406 111 30 (27%)

Sx+RTx vs. RTx 2.034 (0.863 -‐ 4.793) 0.105 101 26 (26%)

HR: Hazard ratio, 95%CI: 95% Confidence interval, Sx: Surgery, RTx: Radiotherapy,

Sx+RTx: Surgery and adjuvant radiotherapy, Diff: Differentiated, %n: failures as percent of

sample size

Each row represents a separate univariate comparison. All variables satisfy the

proportional hazards assumption, i.e. the ratio of the KM curves is approximately

70

constant over the study period. One of the properties of proportional hazards is that the

values of the predictors are independent of time. Schoenfeld has devised an approach,

which allows “residuals” from the model to be calculated for each covariate. If the

correlation between the outcome (time to event) and the residual for a particular

covariate is not significantly different from zero this indicates that the covariate is

independent of time and the proportional hazards assumption is satisfied.(71)

Table 19 shows there are no statistically significant treatment comparisons in predicting

survival in the univariate model. I will investigate this further with the addition of

potential confounding variables and using time dependent Cox regression. However the

95% CI are wide for these treatment comparisons. Of interest is the treatment

comparison of Sx+RTx vs. RTx, in which the p value is 0.105 and the true effect may be

low as 0.86 (i.e. a 14% reduction in risk of death for Sx+RTx) or as high as 4.8 (a 4.8 fold

increase in the risk of death for Sx+RTx) with the actual effect size being 2 fold. A larger

number of patients may well produce a significant result for this trend (P<0.05) and

further investigation of this comparison may be warranted. The current study may not

have an adequate power to detect a 2-‐fold increase, which is still clinically significant.

Interpretation of risk reduction

If the estimated HR is less than 1 then it is possible to calculate the risk reduction by

subtracting the HR from one and expressing it as a percentage. For example a HR of 0.75

represents a 100 X (1-‐0.75)=25% reduction in the risk of the event. A HR between 1 and 2

is interpreted by simply subtracting 1 from the HR and expressing the result as a

percentage. Thus a HR of 1.79 represent a 100 X (1.79-‐1)=79% increase in the risk of the

event. A HR above 2 is usually interpreted as representing a 2 fold in the increase in risk.

71

Interpretation: Univariate Results

Increasing tumour size, increasing age at diagnosis, age greater than 70 years and

tumours that are classified as T2 (>20mm) were significant predictors of worse survival. A

patient whose tumour classification was T2 or greater had approximately a 2-‐fold

increase in the risk of dying at any given time compared to patients with T1 tumours. This

increase in risk ranges from a low of 17% to a high of 3.85 fold.

Patients older than 70 had a 2.8 fold increase in the risk of dying compared to those

under 70. The increase in risk ranges from a low of 56% to a high of 5.18 fold. There is a

4% increase in the risk of death for each increased year of age. The range of the risk

increase is from 2-‐6%.

These variables (except for age as a continuous variable) are presented in Figures 4 and 5

together with the corresponding log-‐rank test.

72

Variable: T1

Figure 4 Cumulative proportion experiencing the event for the tumour size as a predictor

of survival

From Figure 4 patients with tumours with a clinical size ≥ 20 mm had a decreased survival.

In these patients (≥T2) there was an approximate 50% reduction in the risk of dying

compared to if the tumour was smaller than 20 mm (T1). This is a significant survival

reduction and persists throughout the entire follow-‐up period.

Logrank P value: 0.011

HR(≥T2:T1)=0.471 (95%CI: 0.26-0.853)

≥T2

T1

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144 132 116 93 71 48T146 38 31 22 18 15≥T2

Numbers at risk

0 12 24 36 48 60Time from diagnosis (months)

Tumour size as a predictor of overall survival

73

Variable: Age≥70 (Age greater than or equal to 70 years)

Figure 5 Cumulative proportion experiencing the event for the variable of age (age≥70

years) as a prognostic indicator of survival

Patients older than 70 had a 2.85 fold risk increase in dying, when compared to patients

below 70 years of age. This risk reduction for those patients below 70 years of age is

statistically significant.

Other variables considered were: side of tumour (left, right), smoking status (never

smoked or smoked), gender (males, females), tumour site (lower lip, upper lip, or

commissure) and tumour differentiation (well, moderate, or poor). A univariate analysis

of each of these variables did not show any statistical significance.

Logrank P value: <0.001

HR(Age≥70:<70)=2.849 (95%CI: 1.566-5.181)

Age≥70

Age<70

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70 62 54 38 28 16Age<70120 108 93 77 61 47Age≥70

Numbers at risk

0 12 24 36 48 60Time from diagnosis (months)

Age as a predictor of overall survival

74

Recurrence models from diagnosis

Table 20 Univariate results for recurrence modelling

Recurrence from Diagnosis to Death

Variable HR 95%CI P value N failures (%n)

Age

1.006 (0.99 -‐ 1.022) 0.485 189 55 (29%)

Age≥70 1.454 (0.847 -‐ 2.498) 0.175 189 55 (29%)

T1 tumour 0.919 (0.492 -‐ 1.716) 0.79 189 55 (29%)

Left sided tumour 1.319 (0.755 -‐ 2.304) 0.331 189 55 (29%)

Smoker (ex or current) 0.81 (0.454 -‐ 1.446) 0.477 172 49 (28%)

Male 1.101 (0.579 -‐ 2.094) 0.77 189 55 (29%)

Lower lip 0.903 (0.407 -‐ 2.003) 0.802 189 55 (29%)

Well diff 0.688 (0.375 -‐ 1.263) 0.228 153 43 (28%)

Treatment HR 95%CI P value N failures (n%)

Sx vs. RTx 3.529 (1.882 -‐ 6.617) <0.001 168 40 (24%)

Sx or Sx+RTx vs. RTx 2.702 (1.444 -‐ 5.056) 0.002 190 48 (25%)

Sx+RTx vs. Sx 0.071 (0.01 -‐ 0.515) 0.009 111 30 (27%)

Sx+RTx vs. RTx 0.284 (0.037 -‐ 2.185) 0.227 101 26 (26%)

HR: Hazard ratio, 95%CI: 95% Confidence interval, Sx: Surgery, RTx: Radiotherapy,

Sx+RTx: Surgery and adjuvant radiotherapy, All variables satisfy the proportional hazards

assumption

We see from the previous analyses that apart from differences in the treatment

comparisons, no other variables were significant for predicting recurrence. Three of the

four treatment comparisons were statistically significant. When presenting the HR for

75

these comparisons the second treatment detailed refers to the comparative group. For

example when comparing Sx to RTx a HR of 3.5 suggests that patient undergoing Sx have

a 3.5 higher risk of recurrence compared to those undergoing RTx (the comparative

group). Similarly, patients undergoing Sx or Sx+RTx have a 2.7 fold increase in the risk of

developing recurrence compared to those receiving RTx. Patients undergoing Sx+RTx have

a 93% lower risk of recurrence compared to the Sx group. There is no statistical difference

between Sx+RTx compared to those receiving RTx. However the Sx+RTx group had only 22

patients (and a fewer number of recurrences) and this could partly explain the non-‐

significant result.

76

Multivariate analysis

Treatment comparison: Patients treated with Sx vs. RTx

This treatment comparison compares patients receiving either Sx or RTx after adjusting

for potential confounding variables.


treated with Sx alone vs. RTx alone

Variable HR 95%CI P value Number of patients

Survival (Multivariate analysis)

Sx vs. RTx 2.275 (1.106-‐4.679) 0.026 168

T1 0.215 (0.100-‐0.465) <0.001

Age 1.053 (1.029-‐1.078) <0.001

Recurrence (Univariate analysis)

Sx vs. RTx 3.529 (1.882-‐6.617) <0.001 168

HR: Hazard ratio, 95%CI: 95% Confidence interval, Sx: Surgery, RTx: Radiotherapy, T1: T1

vs. T2, T3, T4 (tumour size); Age in years. All variables in both models satisfy the

proportional hazards assumption

From Table 21, after adjusting for tumour size and age, there is a 2.3 fold increase in risk

of dying for patients receiving Sx compared to RTx with survival as the outcome. When

tested univariately (Figure 6), treatment with RTx was not statistically significant

compared to Sx with respect to survival, but in the multivariate model this was

statistically significant. Tumour size and the age at diagnosis were also statistically

significant. A possible explanation for this apparent anomaly is that patients undergoing

77

RTx were older (from Table 17 the mean age: RTx: 63.5, Sx: 58.7), with a higher

proportion of tumours T2 or greater (RTx: 67%, Sx: 26%).

Note in the univariate analysis, when considering recurrence as the outcome, treatment

with RTx showed a significant reduction in the risk of recurrence compared to treatment

with Sx alone. No other variables were significant.

Survival

Figure 6 Cumulative proportion experiencing the event (death) for Sx alone vs. RTx alone

in predicting overall survival

The survival risk for patients having Sx is not statistically significantly different compared

to patients undergoing RTx. The two survival curves cross between 24 and 36 months

twice. The P value for this comparison is P=0.271.


HR(Sx:RTx)=1.435 (95%CI: 0.752-2.738)

Surgery

Radiotherapy

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89 81 68 49 36 24Surgery only79 69 60 52 42 33Radiotherapy only

Numbers at risk

0 12 24 36 48 60Survival time from diagnosis (months)

Surgery vs. Radiotherapy (survival)

78

Recurrence

Figure 7 Cumulative proportion experiencing the event (recurrence) for Sx alone vs. RTx

alone in predicting time to recurrence.

Patients who underwent RTx had a lower risk of recurrence (LR or DRR) compared to

those who received Sx. There was a 3.53 fold increase in the risk of recurrence observed

in Sx patients, which was statistically significant. The curves did not separate before 12

months after which a distinct benefit becomes evident and is maintained throughout the

rest of the follow up period. However, despite this apparent benefit in reduced

recurrence for patients undergoing RTx, this did not translate into a OS benefit. This is

because these patients often do not die of lip cancer but die of some other cause such as

heart disease. Our database was not linked to the deaths registry nor were we informed

how a patient died and therefore we could not determine the cause of death. However in

the literature death caused by lip cancer is not common.

Logrank P value: <0.001

HR(Sx:RTx)=3.529 (95%CI: 1.882-6.617)Sx

RTx

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89 71 42 27 19 11Sx patients79 61 53 44 34 25RTx patients

Number at risk

0 12 24 36 48 60Time to recurrence from diagnosis (months)

(recurrence)Surgery vs. Radiotherapy

79

However, patients who recur have increased risk of death compared to those who do not

recur. This was found by examining recurrence dates as a time varying covariate in

survival analysis. Patients who recur have 2.03 times increased risk of death compared to

those who had not recurred. This was statistically significant. (HR: 2.03; 95%CI: 1.13-‐3.66;

P=0.018).

Treatment comparison: Patients treated with Sx or Sx+RTx compared to RTx

This comparison refers to those patients who had any Sx compared to those receiving

RTx. All patients are represented in this analysis.

Table 22 Survival and recurrence models for treatment comparison between patients

treated with Sx alone or with adjuvant RTx vs. RTx alone

Variables HR 95%CI P value


Sx or Sx+RTx vs. RTx 2.512 (1.274-‐ 4.954) 0.008

T1 0.183 (0.091-‐0.367) <0.001

Age 1.050 (1.029-‐1.073) <0.001


Sx or Sx+RTx vs. RTx 2.702 (1.444-‐5.056) 0.002

Sx: Surgery, RTx: Radiotherapy, Sx+RTx: Surgery and adjuvant radiotherapy, T1: T1 vs. T2,

T3, T4 (tumour size); Age in years.

After adjusting for tumour size and age, there was a 2.5 fold increase in the risk of dying

for patients having Sx or Sx+RTx compared to RTx (Table 22). On univariate analysis RTx

was not statistically significant from Sx or Sx+RTx with respect to survival, but in the

80

multivariate model this comparison was statistically significant. The possible explanation

for this is similar to that for Sx vs. RTx as previously mentioned, i.e. patients receiving RTx

were older than those having Sx and of similar age to those having Sx+RTx (from Table 17

the mean age: RTx: 63.5, Sx+RTx: 64.9, Sx: 58.7). There were a markedly higher

proportion of ≥T2 tumours in the RTx cohort as well (T2 proportion: RTx: 67%, Sx: 26%,

Sx+RTx 7%). Tumour size and the age at diagnosis are also statistically significant in

predicting survival on univariate analysis.

In the recurrence model there was a 2.7 fold increase in risk of recurrence for patients

receiving Sx or Sx+RTx compared to those receiving RTx. No other variables were

significant.

Below are the curves showing the cumulative proportion experiencing death or

recurrence for the treatment comparison of Sx or Sx+RTx vs. RTx and the corresponding

logrank tests.

81

Survival


predicting survival

There was no statistically significant difference between patients who received RTx

compared to those who received Sx or Sx+RTx with respect to OS. However, Figure 8

shows the two curves cross at approximately 24 months. To explore this further, a

proportional hazards model was fitted where the time axis was divided at 24 months and

treatment effects estimate before and after this cut-‐off point. This is referred to as a time

dependent Cox analysis and the results are given in Table 23(a).


HR(Sx or Sx+RTx: RTx) =1.557 (95%CI: 0.847-2.862)

Sx or Sx+RTx

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111 101 87 63 47 30Sx or Sx+RTx patients79 69 60 52 42 33RTx patients

Number at risk


(overall survival)Surgery +/- adjuvant radiotherapy vs. Radiotherapy

82

Table 23(a) Time dependent Cox analysis at 24 months

Sx or Sx+RTx vs. RTx HR 95%CI P value

<24 months 0.438 (0.143 -‐ 1.338) 0.147

≥ 24 months 2.776 (1.250 -‐ 6.165) 0.012

HR: Hazard ratio, 95%CI: 95% Confidence interval, Sx: Surgery, RTx: Radiotherapy, Sx+RTx:

Surgery and adjuvant radiotherapy

Table 23(a) shows that OS between patients having Sx or Sx+RTx vs. RTx was not

significantly different before 2 years of follow up although there is a 56% benefit for

patients receiving Sx or Sx+RTx. However, after 2 years there is a 2.8 fold increase in the

risk of death for patients receiving Sx or Sx+RTx and this increase in risk was significant.

This may be because patients undergoing RTx were older and after removing those who

could not survive at least 2 years (i.e. those who were very sick to start with from multiple

co-‐morbidities) the remaining healthier cohort may have benefitted more from RTx as

opposed to those receiving Sx or Sx+RTx.

Table 23(b) shows that there are 75% and 77% patients surviving longer than 2

years in the RTx and Sx or Sx+RTx cohorts respectively. Of those patients surviving

more than 2 years those who were treated with RTx alone had improved survival

compared to the other cohorts.

83

Table 23(b) Summary of patients; based on 2 yr survival.

Treatment Alive > 2yrs N Age Range

RTx Alive ≥ 2 yrs with followup 59 60.6 27-‐97

RTx Died < 2 yrs 8 75.2 55-‐93

RTx Censored and followup < 2 yrs 12 69.8 45-‐93

Sx or Sx+RTx Alive ≥ 2 yrs with followup 86 60.9 17-‐88

Sx or Sx+RTx Died < 2 yrs 5 69.5 58-‐81

Sx or Sx+RTx Censored and followup < 2 yrs 20 53.5 20-‐81

Sx: Surgery, RTx: Radiotherapy, Sx+RTx: Surgery and adjuvant radiotherapy, n: number of

patients, yrs: years; Age in years.

Recurrence


predicting recurrence.

Logrank P value:0.001

HR(Sx or Sx+RTx:RTx)=2.702 (95%CI: 1.444-5.056)

Sx or Sx+RTx

RTx

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110 91 60 40 29 16Sx or Sx+RTx patients79 61 53 44 34 25RTx patients

Number at risk


(recurrence)Surgery +/- adjuvant radiotherapy vs. Radiotherapy

84

Figure 9 shows a significant difference in time to recurrence for patients who received

RTx compared to Sx or Sx+RTx. There was a 2.7 fold increase in the risk of recurrence for

patients receiving Sx or Sx+RTx compared to patients having RTx. In the first year of follow

up there was no discernable difference between the two groups as the curves are

essentially the same.

Treatment comparison: Patients receiving Sx+RTx vs. Sx.


patients receiving Sx+RTx vs. Sx.

Variable HR 95%CI P value

Survival (Univariate analysis)

Sx+RTx vs. Sx 1.415 (0.625-‐3.205) 0.406

Recurrence (Multivariate analysis)

Sx+RTx vs. Sx 0.059 (0.008-‐0.434) 0.005

Age 1.019 (1.000-‐1.038) 0.050

HR: Hazard ratio, 95%CI: 95% Confidence interval, Sx: Surgery, Sx+RTx: Surgery and

adjuvant radiotherapy, Age is in years.

This treatment comparison is different to the previous treatment comparison in that

Sx is being compared to Sx+RTx and all patients in this comparison have undergone

surgery, whereas the previous comparison was between those patients who had

received surgery (+/-‐ RTx) versus those not having any surgery.

In the model for recurrence, age did not satisfy the proportional hazards assumption

(P=0.02). However, I still chose to include age in the multivariate model, as its significance

was borderline.

85

Table 24 did not show any difference in survival for patients having Sx+RTx compared to

those having Sx. There were no other variables that were significantly related to survival.

Patients who underwent Sx+RTx had a 94% lower risk of recurrence compared to patients

who were treated by Sx alone after adjusting for age. This risk reduction was also

statistically significant. Age in this model was of borderline significance (P=0.05) and there

was a 2% increase in risk of recurrence for each year of age.

Below are the estimated survival and recurrence curves showing the cumulative

proportion experiencing death or recurrence and the corresponding log-‐rank test

comparing Sx+RTx vs. Sx.

Survival

Figure 10 Cumulative proportion experiencing the event for Sx+RTx vs. Sx alone in

predicting survival

Logrank P value: 0.403HR(Sx+RTx:Sx)=1.415 (95%CI: 0.625-3.205)

Sx+RTx

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22 20 19 14 11 6Sx+RTx patients89 81 68 49 36 24Sx patients

Number at risk


(overall survival)Surgery and adjuvant radiotherapy vs. Surgery

86

Figure 10 shows the curves for Sx+RTx and Sx overlapping up until 24 months of follow

up, where after they separate. However, the log-‐rank test was not statistically significant.

The separation observed between the two groups after 24 months is also not statistically

significant (P=0.403) as demonstrated with a time dependent Cox result.

Recurrence

Figure 11 Cumulative proportion experiencing recurrence for Sx+RTx vs. Sx

Patients having Sx+RTx had a significantly lower recurrence rate with a risk reduction of

93% compared to those who having Sx alone. It should be noted however that the

number of patients in the Sx+RTx group was small (21 patients, ¼ of the number in the Sx

group). This unbalanced selection of patients and the ensuing number of events in each

group may potentially explain the wider CIs for the HR (effect size).

Logrank P value: <0.001HR(Sx+RTx:Sx)=0.071 (95%CI: 0.01-0.515)

Sx

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21 20 18 13 10 5Sx+RTx patients89 71 42 27 19 11Sx patients

Number at risk


(recurrence)Surgery and adjuvant radiotherapy vs. Surgery

87

Treatment comparison: Patients receiving Sx+RTx vs. RTx


patients receiving Sx+RTx vs. RTx

Variable HR 95%CI P value


Sx+RTx vs. RTx 3.112 (1.189-‐8.143) 0.021

T1 0.342 (0.140-‐0.834) 0.018


Sx+RTx vs. RTx 0.284 (0.037-‐2.185) 0.227

HR: Hazard ratio, 95%CI: 95% Confidence interval, RTx: Radiotherapy, Sx+RTx: Surgery and

adjuvant radiotherapy. T1: T1 vs. T2, T3, T4 (tumour size); All variables in both models

satisfied the proportional hazards assumption.

Table 25 shows that for recurrence there was no statistical difference between the

Sx+RTx and RTx groups. There were no other significant variables related to recurrence.

Patients who had RTx alone had an improved survival compared to those who had Sx+RTx

after adjusting for tumour size. Recall that on a univariate analysis this comparison was

not statistically significant. There is a higher proportion of patients with T1 tumours in

the Sx+RTx group compared to the RTx group (87% vs 60%). Patients with T1

tumours were associated with longer survival (HR:0.471, Figure 4).

Exploring Subgroups

In the subgroup of patients with >T1 tumours the treatment comparison of Sx+RTx vs

RTx was significant, favouring RTx, but this treatment comparison was not significant

88

in patients with only T1 tumours. Also when not adjusting for T stage there was no

significant difference between Sx+RTx or RTx.

In the Sx+RTx cohort the comparison of (T1 vs >T1) in predicting survival was

significant, with a 94% reduction in the rate of death in patients with T1 tumours

compared to patients with >T1 tumours. This was not seen in the RTx cohort. Pooling

all patients in the study (including the Sx cohort as well) patients with T1 tumours

had demonstrated a statistical significant reduction in the risk of death.

This implies that the outcome of patients selected to receive RTx alone in our study is

not influenced by tumour size, even though patients who received Sx+RTx and had T1

tumours had better outcomes. However, the subgroups result is still within the play

of chance. Also RTx and Sx+RTx are not significantly different in terms of survival in

patients with T1 tumours.

Patients who underwent Sx+RTx had a 3.11 fold increase in risk of death (poorer survival)

compared to patients receiving RTx in this model, which was adjusted for tumour size.

The proportion of tumours greater than T1 was higher in the RTx group (Sx+RTx:7%,

RTx:64%).

Curves showing the cumulative proportion experiencing the event for survival and

recurrence on univariate models for Sx+RTx vs. RTx are shown below.

89

Survival


predicting survival

There was no significant difference between the Sx+RTx and RTx treatment arms. The

curves cross at approximately 24 months after which they separate so a time dependent

Cox analysis was performed using a partition of the time axis at 24 months shown in Table

26. Here, there is a significant 4.02 fold increase in survival risk after 24 months with

patients receiving RTx compared to those receiving Sx+RTx, but no difference up to 24

months.

The power of survival analysis is dependent on the number of events in each group. The

Sx+RTx group had 8 deaths compared to the RTx group having 18 deaths. The low total

number of events and uneven event distribution between groups diminishes the

statistical power of the study.

Logrank P value: 0.098HR(Sx+RTx:RTx)=2.034 (95%CI: 0.863-4.793)

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90

Table 26 Time dependent Cox analysis at 24 months

Sx+RTx vs. RTx HR 95%CI P value

<24 months 0.433 (0.054 -‐ 3.459) 0.429

≥ 24 months 4.023 (1.431 -‐ 11.308) 0.008

HR: Hazard ratio, 95%CI: 95% Confidence interval, RTx: Radiotherapy, Sx+RTx: Surgery and

adjuvant radiotherapy

Recurrence


predicting recurrence

Figure 13 shows the curves relating to RTx and Sx+RTx to be distinctly different, however,

this was not statistically different. This lack of difference could possibly be due to low

Logrank P value: 0.197HR(Sx+RTx:RTx)=0.284 (95%CI: 0.037-2.185)

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Number at risk


(recurrence)Surgery and adjuvant radiotherapy vs. Radiotherapy

91

numbers of patients and low failure rates in both treatment groups. This is seen in the

number at risk table, which shows the few patients in the Sx+RTx arm and the lack of

steps (i.e. events) in the Sx+RTx curve.

Risk modelling

So far I have identified the prognostic factors and treatment comparisons impacting on

survival and recurrence. These are namely whether the patient received Sx or Sx+RTx or

RTx, the tumour size and the patient’s age. These variables are now considered in

developing risk models in order to help classify patients based on their risk of survival and

recurrence.

The only treatment comparison that is based on all patients is Sx or Sx+RTx vs. RTx and

only this comparison was used in risk modelling as developed below. If models were

developed for other comparisons: (a) a substantial number of patients would be

excluded, for example the comparison Sx vs. RTx would exclude 21 patients and (b) the

distribution of patients between the treatment groups would be highly skewed, e.g. 21 vs.

79 patients.

For the outcome of recurrence, since there were no other significant variables so only a

model with treatment was developed.

Risk variables in this study were defined as variables that relate to the patient or the

patient’s tumour, or the patient’s treatment. These variables also may effect the

outcomes of recurrence or survival. Risk variables were included if they were statistically

or near statistically significant in their association with survival or recurrence using

backward survival analysis.

92

A Cox proportional hazards model was performed using the identified variables in order

to estimate the coefficient (the natural log of the HR) for each variable. Using these

coefficients for each patient a linear prediction score was calculated using the patient

values for each variable. A cut-‐off point was chosen which classified patients into high and

low risk groups based on the median value of the predictive score. The appropriateness of

this cut-‐off point was confirmed by examining whether the log-‐rank test between the

high and the low groups was statistically significant.

The log-‐rank test is used to show the summary of the difference between the observed

and the expected number of events at each time point when an event occurs. The

expected number of events in each group is a function of the total number of events and

sample size for each group. The null hypothesis that the log-‐rank test addresses is that

the true population survival curves of two or more groups are the same. This is inferred

by comparing the observed and the expected number of events for each group.(72) This

analysis however only demonstrated whether or not the cut-‐off point significantly

separates the two groups. In order to investigate the strength of the association the

following approach was adopted. Patients were classified into high risk or low risk and a

2x2 table created of the recurrence status. This was also repeated using the survival

status. From this 2x2 table an OR and a 95% CI were estimated.

The May-‐Hosmer (MH) goodness of fit test was used to test the adequacy of the risk

model (based on the linear predictor of risk variables). The broad recommendation is that

at least 5 variables are present in any risk model and as we could not identify this number

of significant variables a second goodness of fit test, the GB goodness of fit test not

requiring a minimum number of variables was also performed. The GB goodness of fit test

assumes that a model with good fit, for each group, should have an expected outcome as

predicted by the model similar to the observed outcome. The expected outcome is

calculated from the residuals from the model.(68) The residuals can be interpreted as the

difference between the observed deaths and expected deaths at each time point.(73)

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To calculate the MH goodness of fit test the following approach is used.

(a) An appropriate Cox proportional hazard model is identified.

(b) The risk indicator variable (high and low risk) is added as an extra variate to the

model.

(c) The Cox proportional hazard model is refitted with the variables in (a) together

with the risk indicator variable in (b).

If the coefficient for the risk indicator is not statistically significant, this suggests that the

original variables satisfactorily explain the outcome and the model is an adequate fit.(69)

To help the interpretation and subsequent classification the coefficients were simplified

(including rounding). This simplification, however, was performed so that the

classification of patients into risk groups was preserved. The values of the simplified cut-‐

off point and coefficients are displayed in the profile index (PI) column in the tables

below.

Survival model with treatment

Table 27 Proportional hazards model for the survival risk model including treatment

comparison

Variable Coef. P value 95%CI PI

Sx or Sx+RTx vs. RTx 0.817 <0.05 (0.147 -‐ 1.487) 4

≥T2 1.413 <0.05 (0.727 -‐ 2.100) 8

Age≥70 1.287 <0.05 (0.659 -‐ 1.915) 6

Cut-‐off point 1.3 7

Coef.: Coefficient, Std. Err.: Standard error, 95%CI: 95% Confidence interval, PI: Profile

index, Sx: Surgery, RTx: Radiotherapy, Sx+RTx: Surgery and adjuvant radiotherapy

94

All variables in the model satisfy the proportional hazards assumption. The risk grouping

using the cut-‐off point also satisfies the proportional hazards assumption in a univariate

model.

A high-‐risk patient is defined by factors of the PI (including treatment, tumour size and

age) adding up to more than the cut-‐off point PI of 7. Table 27 shows that to be classified

as a high risk patient the tumour size had to be more than 20 mm in largest dimension or

that the patient was over the age of 70 years and had received Sx, with or without

adjuvant RTx.

Table 28 shows the occurrence of death of patients according to low risk and high-‐risk

groups and an OR, which represents the ratio of the odds of dying among high and low

risk groups. This is obtained using a 2x2 table.

Table 28 2x2 table for risk grouping

Risk group Alive Dead

Low risk 92 19

High risk 50 29

Odds ratio OR: 1.84 P=0.002

95

Table 29 contains the result of the log-‐rank test comparing the risk groups to determine if

the cut point sufficiently divides the groups.

Table 29 Logrank test validating the risk group cut-‐off point

Risk group Events observed Events expected

Low risk 19 29.12

High risk 29 18.88

Total 48 48

P value 0.0025

Null hypothesis: Events observed are equal to events expected in low risk and high-‐risk

groups

The GB goodness of fit shows good fit of the model to the data and in Table 30 we see

that the model accurately predicts the observed events in each group.

Table 30 Gronnesby-‐Borgan goodness of fit test

Group Exp dead Obs dead z = (O-‐E)/ 𝐸 P N Score cut P.I. cut

Low risk 14.7199 19 1.116 0.87 111 1.3 7

High risk 33.2801 29 -‐0.742 0.23 79 1.3 7

Obs: Observed, Exp: Expected, O: Observed, E: Expected, P.I. cut: Profile index cut-‐off

point Null hypothesis: Events observed are equal to events expected in low risk and high-‐

risk groups. A non-‐significant P value indicates good fit.

96

The cumulative proportion of patient experiencing the event (survival) in the two risk

categories is shown in Figure 14.

Figure 14 Risk model of survival for patients who have been treated

Figure 14 shows a significant difference between the two risk groups in terms of survival.

High-‐risk patients have a 2.42 fold risk increase in terms of decreased survival compared

to low risk patients.

When the MH goodness of fit test was performed a number of desirable criteria were not

met. Firstly the minimum number of variables in the model should be five (I only

identified three), secondly the risk score should be ideally divided into quintiles, but this

was impractical in my situation due to the small number of variables. However, the MH

goodness of fit test was performed for completeness.

Logrank P value: 0.003HR(High Risk:Low Risk)=2.416 (95%CI: 1.338-4.36)

High Risk

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97

Table 31 May-‐Hosmer goodness of fit test

Variable Coef. Std. Err. Z P value 95%CI

Sx or Sx+RTx vs. RTx 1.251 0.378 3.3 0.001 (0.509 -‐ 1.993)

≥T2 2.359 0.526 4.49 <0.001 (1.329 -‐ 3.389)

Age≥70 1.909 0.409 4.67 <0.001 (1.108 -‐ 2.710)

Risk indicator (RI) -‐1.219 0.533 -‐2.29 0.022 (-‐2.264 -‐ -‐0.175)

Coef.: Coefficient, Std. Err.: Standard Error, 95%CI: 95% Confidence Interval, PI: Profile

index, Sx: Surgery, RTx: Radiotherapy, Sx+RTx: Surgery and adjuvant radiotherapy, Test

does not indicate good fit as P value for RI is significant.

In Table 31, I present the coefficients of the log HRs as the linear prediction score is a

function of the coefficient from the model rather than the HRs themselves. Ideally the

coefficient for the risk score (RI) should not be statistically significant for a good fit. In this

case however, it was statistically significant which is probably due to the small number of

variables in the model and using only 2 risk groupings.

Survival model not including treatment

This model helps classify the risk of patients prior to receiving any treatment. The survival

risk model excluding the treatment variable in Table 32 shows the PI and cut-‐off point.

Patients with tumour size greater than 20 mm in largest dimension or age greater than 70

years are at high risk. Both variables are significant predictors in the survival model.

98

Table 32 Proportional hazards model for the survival risk model excluding treatment

comparison

Variable Coef. P value 95%CI PI

≥T2 1.074 0.001 (0.450 – 1.699) 1

Age≥70 1.298 <0.001 (0.667 – 1.928) 1.2

Cut-‐off point ≥1 1


index

All variables in the model in Table 32 satisfy the proportional hazards assumption. The

risk grouping also satisfies the proportional hazards assumption in a univariate model.

Table 33 shows the 2x2 table from which the OR is calculated and the corresponding

significance for the association of the risk indicator variable and occurrence of death.

Table 33 Chi-‐squared test for risk grouping

Risk group Alive Dead

Low risk 77 15

High risk 65 33

Odds ratio OR: 2.61 P=0.006

The log-‐rank test in Table 34 shows that the two risk groups (high and low risk) have

different outcomes with respect to survival and this is statistically significant.

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Table 34 Logrank test validating the risk group cut-‐off point

Events observed Events expected

Low risk 15 24.74

High risk 33 23.26

Total 48 48

P value 0.0042

Figure 15 Risk model of survival excluding treatment variable as a risk factor

Figure 15 shows a significant difference in survival between the two risk groups. High-‐risk

patients had a 2.44 fold increase in risk of dying (worse survival) compared to low risk

patients.


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100

The GB goodness of fit test in Table 35 is satisfied, indicating an adequate fit of the

developed risk model to the data. The expected outcome predicted by the model is not

significantly different to the observed outcome, indicating an adequate fit.


Group Exp dead Obs dead z = (O-‐E)/ 𝐸 P value N Score cut P.I. cut

Low risk 11.255 15 1.116 0.87 92 <1.05 <1

High risk 36.745 33 -‐0.618 0.27 98 >1.05 ≥1

Obs: Observed, Exp: Expected, O: Observed, E: Expected, P.I. Cut: Profile index cut-‐off

point

The MH goodness of fit once again was not satisfied as the risk indicator (RI) term in Table

36 was statistically significant. The RI term is coded 1 for high-‐risk patients and 0 for low

risk patients. Again this is probably due to the number of variables predicting risk being

less than 5, together with the low number of events in each classification of risk and

because there are only 2 risk groupings.

Table 36 May-‐Hosmer goodness of fit test

Variable HR Std. Err. Z P value 95%CI

≥T2 6.142 2.595 4.3 <0.001 (2.683 -‐ 14.058)

Age≥70 9.239 4.655 4.41 <0.001 (3.442 -‐ 24.801)

RI 0.234 0.144 -‐2.36 0.019 (0.07 -‐ 0.784)

HR.: Hazard ratio, Std. Err.: Standard error, 95%CI: 95% Confidence Interval, RI: Risk

indicator variable

101

Recurrence model with treatment

Table 37 shows the proportional hazards model for the proposed risk model with

recurrence as an outcome and treatment included

Table 37 Proportional hazards model for the recurrence risk model including treatment

comparison

Variable Coef. Std. Err. Z P value 95%CI P.I.

Sx or Sx+RTx vs. RTx 1.091 0.329 3.32 0.001 (0.446 -‐ 1.737) 2.5

≥T2 0.426 0.329 1.29 0.196 (-‐0.219 -‐ 1.072) 1

Age≥70 0.426 0.278 1.53 0.125 (-‐0.118 -‐ 0.97) 1

Cut-‐off point 1 2.1


index

Note: All variables in model satisfy the proportional hazards assumption. The risk

grouping also satisfies the proportional hazards assumption in a univariate model.

Patients having Sx alone or Sx+RTx were at higher risk of developing recurrence. No other

patient or tumour factors were significant in determining the risk of a patient. However,

patients with a tumour larger than 20 mm and age ≥ 70 years had increased risk although

these factors did not affect the classification of risk, because their contribution to the PI

was not sufficient to alter the risk classification. Age and tumour size were also not

significant in the proportional hazard model in Table 37, but were included because of the

size of the coefficient (0.426), which may lead to more informed risk discrimination

among patients.

102

Table 38 shows the association between risk groups and recurrence. Here the OR is

calculated with a P value for this risk indicator variable.

Table 38 Chi-‐squared test for risk grouping

No Recurrence Recurrence

Low risk 66 13

High risk 68 42

Odds ratio OR:3.14 P=0.001

Table 39 Logrank test for the risk group cut-‐off point.

Events Observed Events Expected

Low risk 13 24.85

High risk 42 30.15

Total 55 55

P value 0.0012

A statistically significant log rank test is seen in Table 39 indicating that the rate of

recurrence is different in the two risk groups.

103

Figure 16 Risk model of time to recurrence

Figure 16 shows a significant difference in risk of recurrence between the two risk groups.

High-‐risk patients had a 2.7 fold increase in the risk of recurrence compared to low risk

patients. This risk reduction was observed after the 12-‐month follow-‐up period. Until 12

months the high risk and low risk groups share similar levels of risk as the curves

essentially overlap.

Table 40 has the GB goodness of fit test, which shows adequate fit as the expected

number of events as determined by the model was equal to the observed number of

events (P=0.5). In this model the risk indicator variable when added to the original model

(as done previously) was dropped due to collinearity, which prevented the MH goodness

of fit test being performed.


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Group Exp dead Obs dead z = (O-‐E)/ 𝐸 P N Score cut P.I. cut

Low risk 13 13 0 0.50 79 <1 2.1

High risk 42 42 0 0.50 110 ≥1 2.1

Obs: Observed, Exp: Expected, O: Observed, E: Expected, P.I. Cut: Profile index cut-‐off

point

105

Discussion

In this section there will be general discussion relating to the study design. Note that the

significant patient and tumour factors associated with survival or recurrence are 1)

tumour size and 2) age at diagnosis. These factors will be discussed by comparing my

study to the literature; and then the confounding effect they have on treatment

comparisons will be noted. Also, the results of these treatment comparisons will be

viewed in light of the summary of treatment outcomes documented in the literature

review. Finally, the impact of risk modelling will be discussed.

The order of level of evidence for clinical studies, according to the US Preventive Services

Task Force, is as follows:

▪ Level I: Evidence obtained from at least one properly designed randomized

controlled trial. ▪ Level II-‐1: Evidence obtained from well-‐designed controlled trials without

randomization. ▪ Level II-‐2: Evidence obtained from well-‐designed cohort or case-‐control analytic

studies, preferably from more than one center or research group. ▪ Level II-‐3: Evidence obtained from multiple time series with or without the

intervention. Dramatic results in uncontrolled trials might also be regarded as this type of evidence.

▪ Level III: Opinions of respected authorities, based on clinical experience,

descriptive studies, or reports of expert committees.(70)

Our study is a cohort or observational study and is therefore Level II-‐2 evidence. This is

because there are potentially uncontrolled biases, which if they exist can only be partly

addressed via statistical means. Therefore, any inferences regarding treatment benefit

are considered exploratory and would ideally require independent confirmation in a well-‐

designed controlled trial or, failing that, additional observational studies. Nevertheless

106

the hypothesis generating nature of this current study raises potential questions, which

are of interest to clinicians for future investigation and patient risk classification.

RCTs provide the highest level of evidence for treatment benefit and comparisons of

potential prognostic factors as they eliminate or balance bias by virtue of the process of

randomisation. This however is not the case with observational studies and the degree of

bias can generally not be quantified. For example in the current study, patients with

larger tumours may have been excluded from Sx and thus differences between Sx and RTx

could be confounded by tumour size as well as treatment. This may not be completely

accounted for in an adjusted statistical analysis. As another example, after the diagnosis

of lip cancer, the tumour is staged and then patient characteristics are evaluated to

determine the patient’s suitability for Sx and/or RTx. Those who undergo Sx are perhaps

more suitable for Sx over RTx, leading to a selection bias and thereby making comparisons

difficult to interpret.

Another potential bias found in this study is that of referral bias. Referral bias occurs

when patients are preferentially referred to an institute (such as Westmead Hospital

where this study was completed) as there may not be sufficient expertise or facilities in

other centres. Therefore, patients with lip cancer presenting to Westmead Cancer Care

Centre have a higher probability of presenting with more advanced tumours, or be sicker

patients, and are often more complicated to treat. This referral bias may also vary in

degree with different treatment options as patients undergoing RTx require centres to

have RTx facilities, whereas Sx may be performed without such infrastructure, perhaps in

day only surgical units.

Tumour size

Tumour size as a predictor of recurrence and mortality has been investigated in the

literature. In one study by Rodolico et al 2005 (7), there were 97 subjects with a follow up

107

of 5 years looking at time to recurrence. This study had half the sample size of my study

and documented 13 regional failures with no mention of local failure. The reported failure

rate was half that of my study. The HR comparing patients with T1 tumours to higher than

T1 was significant with estimated HR of 15.21 (95%CI: 2.25-‐94.18; P=0.0033), whereas in

my study this was not a significant association. This may be because in my study only 45

of the 189 patients had a tumour size greater than T1. Also the cut-‐off of 20 mm may be

inappropriate in finding statistically significant results. This is because tumour size

recorded as a continuous variable was statistically significant in my study (HR: 1.025;

95%CI: 1.006-‐1.046; P=0.009). A cut-‐off of 20 mm leads to only a minority of patients

above the cut-‐off and many below, which may decrease the power of the analysis

because of uneven groupings. Note that both my study and the study by Rodolico et al

2005 had only 15-‐25% of patients with tumours larger than T1. Despite this uneven

grouping, both studies reported a significant result, although my result was only

significant when using tumour size as a continuous variable.

The study by Rodolico et al 2005 also looked at tumour size as a continuous variable and

found a HR not markedly significantly different to my study (HR = 1.09; 95%CI: 1.05-‐1.13;

P<0.001). Another study (64) using the same dataset as the previous study (7) also found

a significant association between tumour size and recurrence using logistic regression

instead of survival analysis. Here the occurrence of the event, rather than the order in

which events occur, was important (i.e. the data were analysed using logistic regression

with binary data rather than proportional hazards regression with time-‐to-‐event data).

A large 1001 patient study with 34 recurrences documented tumour size dichotomised at

3 cm as statistically significant in predicting for relapse.(5) In this study local control was

not reported. The analysis of data in this current thesis looks at both local and regional

control together. This is important as morbidity and mortality can arise from both local

and regional relapse. Also this 1001 patient study reported an OR and looked only at the

occurrence of relapse and not time to recurrence. The cut-‐off they chose was arbitrary

108

and not justified, as they did not mention why they chose it. This makes it difficult to

compare the results from this study in the literature to the analysis in this thesis.

Another study (57), analysed local control only and reported a significant P value,

although the cut-‐off in tumour size associated with the final reported P value was not

mentioned and could have been from any of the many cut-‐offs they used to tabulate their

groups of patients. Both these studies (5, 57) also documented tumour size to be

associated with regional control and the analysis in this thesis found tumour size (as a

continuous measure) to be associated with local and regional control.

Time to recurrence accounts for the order of an event occurring when comparing groups,

rather than just the number of events occurring, making time to recurrence more

informative. The analysis pertaining to this thesis was only the second study in the

literature to comment on time to recurrence for this particular predictor.

Only one study has investigated tumour size as a predictor of survival.(2) Survival was

measured by the number of deaths rather than survival time in this study. The study

comments on CSS (dying from the lip cancer), not as a HR but as the percentage who

survived. This study had a CSS for tumour size greater than 3 cm at 64% (95%CI: 52%-‐

74%) and less than 3 cm of 92% (95%CI: 89%-‐ 94%) at last follow up. In my study tumour

size was a predictor of survival (time to death of any cause). Also in my study there were

few deaths due to lip cancer itself. Both studies lead to the same conclusion that

increasing tumour size is associated with a worse survival.

Age at diagnosis

There are two studies (5, 61) in the literature that investigated age as a prognostic factor

of recurrence. However, no studies looked at age as a prognostic factor of time to

109

recurrence, death or survival. One study enrolled 251 patients with a 5-‐year minimum

follow-‐up.(61) The authors developed a multiple logistic regression model with age,

tumour area (the area of lip covered by the tumour in square cm) and localisation of the

tumour. They reported age to have a non significant OR (1.013OR; 95%CI: 0.97-‐1.06).(61)

Another study also commented on age,(5) and used age at 40 years old as a cut-‐off. The

study reported that age was not a predictor of recurrence (P=0.99). It is interesting to

note that age did not impact on recurrence and this was also supported in my study, since

it may dismiss the hypothesis that younger patients recur earlier because they have a

more aggressive tumour. There were very few young patients aged below 40 in my study.

This makes it difficult to test whether patients below 40 were more likely to recur.

Importantly older people did not develop recurrence earlier either. The range and

distribution of age as a variable is important. In my study the mean age was 61.4 +/-‐ 16.9

(Standard deviation). Therefore the age distribution may be too narrow to pickup any

difference in outcomes due to age.

No studies have examined age as a prognostic factor of survival in lip cancer patients. My

study showed that increasing age was associated with worsening survival. This was seen

with age as both a continuous and also a binary variable where age was dichotomised at

70 years.

In summary, both tumour size and age are associated with survival in patients with lip

cancer, while only tumour size (measured as a continuous variable) is associated with

recurrence.

Treatment comparison: Sx vs. RTx

Survival for a patient after RTx alone was higher than if they had Sx alone when adjusted

for both tumour size and age. T1 tumours were associated with better survival; whereas

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those patients with higher age at diagnosis had poorer survival. In addition, this

treatment comparison was not significant univariately using the log-‐rank and the

significant P value was only obtained after adjusting for age and size of tumour. This was

somewhat expected as from the baseline demographics the RTx patients were slightly

older and had larger tumours compared to Sx patients. The proportion of patients over

the age of 70 was as follows (RTx: 39.2% & Sx: 31.5%). Also the proportion of patients

with tumour size greater than T1 was as follows (RTx: 39.2% & Sx: 13.5%). This suggests

that tumour size and age may be significant confounding variables in the survival model.

The Sx vs. RTx treatment comparison was significant for recurrence, where patients

receiving RTx alone had a more favourable outcome compared to Sx alone. Therefore

from the data in this thesis, patients who had RTx had better survival and less recurrence

when compared to Sx patients, even though these patients were older and had larger

tumours. The caveat here as mentioned is that this is an observational study that is

subject to more bias than if it were a RCT.

Referring to the medical literature on this treatment comparison (see Summary of

treatment outcome section in this thesis), patients who underwent Sx had an 81.9% 5yr

OS (95%CI: 80.1%-‐83.7%). This CI overlaps with that of RTx which had a 79.9% 5 yr OS

(95%CI: 77.4% -‐ 82.4%). There were 15 studies with a total of 1550 patients documenting

on 5 yr OS for Sx, whereas for RTx there were only 10 studies with 943 patients. Note that

the CI widths are both below 5%, indicating a precise estimate. However my study found

patients receiving RTx to have improved survival compared to Sx after adjusting for

tumour size. Most studies in the literature did not adjust for tumour size and age. Also

few studies in the literature discussed OS in terms of survival time rather than the

occurrence of death. Therefore my study may not be directly comparable as it uses time

to death as an outcome.

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In contrast with respect to recurrence, patients having Sx had increased LRC compared to

RTx patients with CI that do not overlap. Sx patients had 89.8% 5yr LRC (95%CI: 91.6% -‐

87.9%), whereas RTx patients had 85.3% 5yr LRC (95%CI: 88.3% -‐ 82.2%). This suggests

that Sx patients may have better LRC than RTx although the 95% CIs overlap. This is in

contrast to my study and illustrates that my data has different findings from that of other

studies. This could be due to a number of factors. However, at Westmead patients

undergoing RTx generally had larger tumour sizes compared to those undergoing Sx with

tumour size found to be a key predictor of recurrence. Also we evaluated time to

recurrence rather than the event of recurrence so my results cannot be directly

correlated to the literature in the above-‐mentioned summary. Note that there were very

few articles in the literature looking at time to recurrence.

Treatment comparison: Sx and Sx+RTx vs. RTx

This treatment comparison is between patients having any Sx, which includes Sx alone

and patients who received Sx+RTx, compared to patients who received RTx alone. The

treatment effect is significant with RTx associated with an increased survival and a

decreased rate of recurrence when adjusted for tumour size and age in the survival

models. The log-‐rank test shows the treatment comparison is not statistically significant

in the survival model, because this does not adjust for confounding variables. Although

after a time dependent Cox analysis this treatment comparison becomes significant after

24 months but not prior to this.

The recurrence model for Sx or Sx+RTx vs. RTx treatment comparison is statistically

significant. The log-‐rank, which is unadjusted, is significant with separated curves of the

cumulative proportion experiencing an event. Despite patients having Sx being younger

and having smaller tumours, patients receiving RTx still had a lower risk of recurrence, as

seen in the unadjusted recurrence model.

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In addition, age was not a significant confounding variable in the recurrence model and as

such was not included as a confounding variable.

Treatment comparison: Sx+RTx vs. Sx

The treatment comparison between Sx+RTx and Sx revealed no statistically significant

difference in survival. However, in the model dealing with recurrence after adjusting for

age there was a statistically significant effect. The HR for this treatment comparison was

0.059, indicating that patients treated with Sx+RTx had a risk reduction of 94.1%

compared to Sx. This risk reduction was seen in both recurrence models (with and

without adjustment). Age is included in the recurrence model as a confounding variable

as it is a significant variable when included in the model.

There was no survival difference between patients treated with Sx+RTx or Sx, but it must

be noted that most of the deaths in this cohort were not due to lip cancer but from

another cause, which accentuates the importance of the recurrence model.

In the literature from the summary of results by treatment, the CIs for 5yr OS regarding Sx

and Sx+RTx overlap therefore showing no difference. Sx had an 81.9% 5yr OS (95%CI:

80.1% -‐ 83.7%), whereas Sx+RTx had 72% (95%CI: 56.2% -‐ 87.8%). Sx had 15 studies with

1550 patients, whereas Sx+RTx had only 2 studies with 18 patients. Therefore the Sx+RTx

estimate is likely to be imprecise as this is a relatively small sample of patients and the CI

is wide, making any comparison not practical.

Regarding recurrence, the CIs for Sx and Sx+RTx also overlap. Sx had 89.8% 5yr LRC

(95%CI: 87.9% -‐ 91.6%) and Sx+RTx had 95.3% 5yr LRC (95%CI: 88.3% -‐ 100%). Sx had 10

studies with 947 patients, whereas Sx+RTx only had 4 studies with 47 patients. Note that

Sx+RTx had a higher LRC estimate (95.3%) but due to a smaller sample size, the estimate’s

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CIs were wider. With more studies it may be possible that the literature could reflect the

results found in my study whereby patients undergoing Sx+RTx had a lower risk of

recurrence.

Treatment comparison: Sx+RTx vs. RTx

This treatment comparison is between patients who had Sx+RTx and patients who

underwent RTx alone. In the model dealing with survival as an outcome, RTx patients had

increased survival compared to Sx+RTx patients after adjusting for tumour size with a

statistically significant HR. This treatment comparison had a P value of P=0.09 on the log-‐

rank test, which may have been significant had there been more patients in the adjuvant

group as the unadjusted HR was greater than 2. The proportion of patients with tumours

larger than T1 was as follows (RTx: 39%, Sx+RTx: 14%). Therefore patients having RTx had

larger tumours and increasing tumour size was associated with poorer survival. This

makes tumour size a confounding variable in the model analysing Sx+RTx vs. RTx with

regards to survival. Despite the confounding variable being present, when a univariate

time dependent Cox analysis was carried out this treatment comparison was statistically

significant after 24 months (univariately) and favoured RTx.

There was a large but non-‐significant difference in the recurrence models favouring the

treatment Sx+RTx with a HR=0.285 which is approximately a 72% risk reduction. Tumour

size was an important confounding variable in the model with survival as an outcome but

was not present in the model with recurrence as an outcome and age was also not

present in either models.

In the literature, the CIs for Sx+RTx and RTx overlapped indicating no significant

difference. However there were only 2 studies with a total of 18 patients in the Sx+RTx

arm. RTx had 10 studies with a total of 943 patients. Sx+RTx had an estimate of 72.0% 5 yr

OS (95%CI: 56.2% -‐ 87.8%), whereas RTx had 79.9% 5 yr OS (95%CI: 77.4% -‐ 82.4%). Since

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there were so few patients in the Sx+RTx arm it is not practical to make this comparison.

My study provides some guidance on this comparison in that after 24 months of follow up

patients receiving RTx had improved survival compared to Sx+RTx.

Patients having Sx+RTx had 95.3% 5 yr LRC (95%CI: 88.3% -‐ 100%) and RTx had 85.3% 5yr

LRC (95%CI: 82.2% -‐ 88.3%). Sx+RTx had 4 studies with 43 patients and RTx had 5 studies

with 504 patients. With more studies in the Sx+RTx group there is likely to be a significant

difference, where Sx+RTx will result in better LRC than RTx. My study did not find a

significant HR with this treatment comparison most likely also due to low sample size in

the Sx+RTx arm however the direction of the HR was the same as that seen in literature.

Risk models

The risk models in this thesis are designed to classify patients into risk groups based on

their patient, treatment and tumour factors with the outcome of survival or recurrence.

There are two survival risk models: one including a treatment comparison, and one

without a treatment comparison. As discussed, the survival model without a treatment

comparison is used to classify risk in patients not yet assigned to a treatment. This is done

to quantify the risk of a patient regardless of the treatment they undergo. The model

therefore gives an understanding of which risk factors are attributable to a high-‐risk lip

cancer patient.

The survival model including a treatment comparison is used to classify patients into risk

groups after they have undergone a specific treatment. The treatment comparison adds

more information to the classification of risk in patients.

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There is only one recurrence risk model and this includes a treatment comparison. There

were insufficient patient and tumour factors of predictive value to construct a recurrence

risk model excluding the treatment comparison. Only the Sx or Sx+RTx vs. RTx treatment

comparison was used as all patients were represented in this comparison. This also avoids

multiple testing which would inflate the type-‐1 error if all treatment comparisons were

assessed. To my knowledge there is no evidence in the literature regarding risk models on

lip cancer and this makes my study novel in this respect.

Only two groups were allocated in the risk model with one cut-‐off point due to the

minimal number of predictors in the model. This is not in accordance with the paper by

May and Hosmer, 1998 (69) that recommends at least five groups for a 200 patient

database or more. Due to the event distribution among the few variables, this was not

practical.

Risk model: Survival with treatment

In this risk model, three variables are included. They are the treatment comparison Sx or

Sx+RTx vs. RTx, the tumour variable ≥T2 (which is positive for tumours greater than T1,

i.e. largest dimension greater than 20 mm) and the patient variable age ≥70.

If the patient’s tumour size was ≥T2 they were automatically in the high-‐risk group. If they

have a T1 tumour then to be classified as high risk they must be both over 70 years of age

and have had Sx either alone or followed by adjuvant RTx.

Interestingly the size of the tumour is the main risk factor that predicts survival and also

independently determines risk. The implication here is that regardless of the treatment

undergone by the patient, if the patient has a T2 or higher tumour classification then they

are at high risk in terms of survival.

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Treatment with Sx or Sx+RTx and an age >70 in combination are required to define a poor

prognosis in patients with T1 tumours. Therefore patients diagnosed at <70 years of age

and not treated with Sx or Sx+RTx are not at low risk in terms of survival. Similarly

patients >70 years of age at diagnosis who were treated by RTx were classified as low risk

also.

Risk model: Survival without treatment

This risk model did not include a treatment comparison. It is useful for classifying patients

based on patient and tumour factors alone. There are only two variables of interest in this

model and both are independent risk factors in determining high risk. They are: ≥T2

status (tumour greater than T1) and age ≥70. Either one being present is enough to

classify a patient as high risk.

Patients over the age of 70 with T1 tumours are classified as high risk based on patient

and tumour factors and this inherent risk does not change with treatment. However

when classifying patients based on patient, tumour and treatment factors if the same

patient was to be treated by RTx then they would be classified as low risk. This is an

interesting distinction to be made as for this cohort RTx may be more effective than Sx.

This is likely because older people with perhaps multiple co-‐morbidities are less suitable

for Sx and its associated anaesthetic risk.

The two survival models developed can be used to aid in clinical decision-‐making. The

survival prognostic risk model not taking into account treatment guides a clinician in

determining risk for the patient, to help determine treatment choice. This is a baseline

risk and is also useful in determining patient eligibility for RCTs.

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The second model is the survival prognostic risk model that takes into account treatment

given. This model allows the risk to be determined accounting for potential treatment

choice. If the risk level of the patient changes based on potential treatment, this can aid

the clinician in deciding on patient management. Note prognostic risk modeling that this

is not the only criterion for treatment selection.

From the survival risk models in this study, the following can be inferred. If the patient

has a tumour size greater than T1 then they are classified as high risk, regardless of other

characteristics. If they are older than 70 years of age and have a T1 tumour they also have

a high baseline risk. If these patients are treated with radiotherapy alone, then their risk

changes from a high baseline risk to low risk after radiotherapy treatment.

Risk model: Recurrence with treatment

Due to lack of sufficient patient or tumour risk factors only one recurrence model could

be constructed and this incorporates a treatment comparison. The Sx or Sx+RTx vs. RTx

treatment comparison is the only risk factor that independently and necessarily classifies

a patient for risk of recurrence, with Sx or Sx+RTx being associated with high risk.

Regardless of other risk factors, if the patient received RTx alone, they would be in the

low-‐risk category for developing recurrence. Once again this does not imply that all

patients should be treated with RTx as this is not a RCT and the patient may not be

suitable for RTx.

Other risk factors that add to risk but do not independently attribute to high risk are ≥ 70

years of age and having tumour >T1.

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Conclusion

In this thesis the risk factors for developing lip cancer, and predictors of recurrence and

survival have been discussed and analysed. A summary of treatment results based on

multiple outcomes was also performed. Based on a literature review the risk factors were

re-‐evaluated using the Westmead data with respect to survival and risk of recurrence as

outcomes. I report tumour size and age as significant predictors of survival and

recurrence.

My study also found that patients undergoing RTx had an increased survival and

decreased risk of developing recurrence compared to Sx. The literature suggests that Sx is

associated with achieving a better LRC then with RTx but this finding was not statistically

significant from the summary of treatment outcomes in the literature review. Note that

the majority of studies investigating treatment outcomes for lip cancer did not account

for the order of recurrence or death (time to event) among the treatment outcomes but

rather looked at the total counts (using an OR). The order of event occurrence is

important as the associated HR reflects the risk of recurrence or mortality throughout the

study follow-‐up, whereas an OR from total counts reflects the final risk at conclusion of

the follow-‐up. Therefore time to event studies may be more informative.

Furthermore, in respect to survival, patients undergoing RTx had larger tumours and were

older at diagnosis. Both tumour size and age are predictors of worse survival. Larger

tumour size is also a predictor of recurrence although patients undergoing RTx had lower

rates of recurrence compared to Sx patients in my study. Therefore despite the

confounding variable distribution (tumour size and age) favouring patients having Sx,

patients experienced a better survival and lower risk of recurrence if they had RTx.

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Sx+RTx did not appear to improve survival compared to Sx, however there was a 94.1%

risk reduction of recurrence.

The risk models constructed in this study do not have enough variables or risk categories

to adequately classify risk for patients diagnosed with lip cancer. The risk models would

need to be validated on another lip cancer cohort to determine their generalisability.

These results may assist in the design of future RCTs for comparing treatments. I suggest

such trials should be stratified according to age and tumour size. However there is no

equipoise to devise an RCT. Equipoise is lacking because patients with large tumours are

more likely to receive RTx. Similarly it is common to perform Sx for small tumours in order

to avoid the consequences of RTx such as extended treatment and local side effects.

Therefore there is little common ground for the treatments to be compared.

More studies that report on Sx+RTx patients are needed for better estimates of their

outcomes. Currently there are only a few studies all with relatively lower patient numbers

dealing with Sx+RTx patients. Therefore this current study adds to the existing literature

for Sx+RTx patients by providing more information on the outcome for Sx+RTx patients

regarding survival and recurrence.

Future studies should also comment on a HR and have standardised reporting for better

comparison between studies. This includes agreeing on a common cut-‐off for age and

tumour size and also performing analyses on continuous measures of tumour size and

age. There should also be more studies using multivariate analyses as factors such as

tumour size and age are confounding variables that need to be adjusted for when making

treatment comparisons. Analysis should include histological data where possible as this

could also be a potential confounding variable.

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